LTS QA

How can we obtain the highest quality in our Artificial Intelligence/Machine Learning? The answer is high-quality training data, according to many scientists. But to ensure such high-quality work might not be that easy. So the question is “What is the data annotation best practices?”

One might think of data annotation as mundane and tedious work that requires no strategic thinking. Annotators only have to work on their data and then submit them!

However, the reality speaks differently. The process of data annotation might be lengthy and repetitive, but it has never been easy, especially for managing annotation projects. In fact, many AI projects have failed and been shut down, due to the poor quality of training data and inefficient management.

In this article, we will guide you through the Data annotation best practices to ensure data labelling quality. This guide follows the steps in a data annotation project and how to successfully and effectively manage the project:

1. Define and plan the annotation project
2. Managing timelines
3. Creating guidelines and training workforce
4. Feedback and changes

 

1. Define and plan the annotation project

Every technological project needs to start with the defining and planning step, even for a seemingly easy task like data annotation.

First off, there needs to be the clear clarification and identification of the key elements in the project, including:

• The key stakeholders
• The overall goals
• The methods of communication and reporting
• The characteristics of the data to be annotated
• How the data should be annotated

 

 

The key stakeholders

With the key stakeholders, there are mainly three of them:

• The project manager of the whole AI product: It is a must for project managers to determine are the ones to set out the practical application of the project, and how what kinds of data need to be put into the AI/ML model.
• The annotation project manager: His/her main duties include the day-to-day functions, and they will be responsible for the quality of the outputs. They will work directly with the annotators and conduct necessary training. When you have an annotation project manager, make sure that they have subject matter expertise so that they can start working on the project right away.
• The annotators: For the annotators, it is best that they are well-trained of the labeling tools (or the auto data labeling tool).

After identifying the stakeholders, you can easily set out their responsibilities. For example, the overall quality of the datasets will be the responsibility of the annotation project manager, but how the data is used in the AI/ML model will be solely on the project manager.

Each of these stakeholders has their own job, their own skill sets and their valuable perspective to achieve the best result. If your project lacks any of these stakeholders, it can be at risk of poor performance.

 

The overall goals

For any data annotation project, you need to know what you want as an output, hence developing the appropriate measures to achieve it. With the key project stakeholders, the project manager can put all of their input together and come up with the overall goals.

 

 

To come up with the overall goals, you need to answers to these:

• The desired functionality
• The intended use cases
• The targeted customers

Once the overall goals are clarified, the next step of the annotation project will be more projected and well-defined, making the working process easier.

 

The methods of communication and reporting

It is quite all over the place when it comes to communication and reporting in data annotation projects. Communication in software development seems to be much more emphasized than in data annotation, but it doesn’t mean that the communication is of less significance.

Maybe the communication among the annotators is thin but between the annotators and project manager or the annotation manager, it is not the case. In fact, they need to constantly keep track of each other’s work to ensure the overall quality.

Therefore, the use of communication platforms and reporting app is very important.

• For communication, the project manager can choose from Scrum, Kanban or the Dynamic Systems Development Method.
• For reporting, the annotation manager needs to establish a system of controlling the quality and quantity of the annotators. The simplest, yet very effective way is through Excel or Google Spreadsheet.

 

The characteristics of the data to be annotated

The stakeholders need to understand the following:

• The features
• The limitations
• The patterns

With the initial understanding of the data, the next vital step is to sample for data annotation and whether any pre-processing of the dataset is needed

With any project that has a big sum of data, the annotation manager needs to break down the project into small parts for trial. With microprojects like this, the annotators don’t necessarily need the subject matter expertise to carry out.

Check out: Data Annotation Guide

 

2. Managing timeline

The timeline is another important feature that needs to be well taken care of. Every stakeholder will have to be involved in this process to define the expectations, constraints and dependencies along the timeline. These features can have a great impact on the budget and the time spent on the project.

 

 

There are some ground rules for the team to come up with a suitable timeline:

• All stakeholders have to be involved in the process of creating a timeline
• The timelines should be clearly stated (the date, the hour, etc.)
• The timelines must also include the time for training and creating guidelines.
• If there are any issues or uncertainties related to the data and the annotation process should be communicated to all stakeholders and documented as risks, where applicable.
• In this process, the timeline will be decided as follows:
• For the product managers, they must take into account the overall requirements of the project. What are the deadlines? What are the requirements and the user experience? Since the product managers don’t directly get involved in the data annotation process, they need to know or be educated about the complexity of the project, hence setting reasonable expectations.
• For the annotation managers, they need to know the project’s complexity to allocate the annotators need to know to do the project. What is the subject matter knowledge required with this project? How many people are required to do this? How do they ensure the high-quality and follow the timeline effectively? These are the questions that they need to answer.
• For the data annotators, they need to clarify what type of data they’re working on, what types of annotation and the knowledge required to do the job. If they don’t have them, it is a must that they are trained with an expert.

Check out: Data Annotation Working Process

 

3. Creating guideline and training workforce

Before stepping into the annotation process, you must consider the guideline and the training so that the team can achieve the highest quality in their work.

 

Creating guideline

For the data annotated to be consistent, the team needs to come up with a full guideline for one particular data annotation project.

This guideline should be built based on all of the information there is about the project. If you have similar projects like this, you should also write the new guideline based on it.

 

 

Here are some ground rules for creating a guideline in data annotation:

• The annotation project manager needs to put the complexity and the length of the project in mind. Especially with the complexity of the project will affect the complexity of the guideline.
• Both tool and annotation instructions are to be included in the guideline. Introduction to the tool and how to do it must be clearly stated.
• There must be examples to illustrate each label that the annotators have to work with. This helps the annotators understand the data scenarios and the expected outputs more easily.
• Annotation project managers should consider including the end goal or downstream objective in the annotation guidelines to provide context and motivation to the workforce.
• The annotation project manager needs to make sure that the guideline is consistent with other documentation of the project so that there will be no conflict and confusion.

 

Training workforce

Based on the guideline that stakeholders have, the annotation team manager now can continue with the training easily.

Again, don’t think of the annotation as easy work. It can be repetitive but also requires much training and subject matter knowledge. Also, training for the data annotators requires attention to many matters, including:

• The nature of the project: Is the project complicated? Does the data require subject matter knowledge?
• The project’s time frame: The length of the project will define the overall time spent on training
• The resources of the individual or group managing the workforce.

After the training process, the annotators are expected to adequately understand the project and produce annotations that are both valid (accurate) and reliable (consistent).

 

 

During the training process, the annotation manager needs to make sure that:

• The training is based on one guideline to ensure consistency.
• If there is a case of new annotators joining the team when the project has already started, the training process will be done again, either through direct training or training in recorded video.
• If there is any question, all of them have to be answered before the project has started.
• If there is confusion or misunderstanding, it should be addressed right at the beginning of the project to avoid any errors later.
• The matter of quality output must be clearly defined in the training process. If there is any quality assurance method, it should be announced to the annotators.
• Written feedback is given out to the data annotators so they know what metrics they are going to work on.

 

During the annotation process, the quality of the training datasets relies on how the annotation manager drives the annotation team. To ensure the best result, you can take the following measures:

• After the requirements of the project are clarified, you need to set reasonable targets and timelines for the annotators to achieve.
• Every estimation and pilot phase needs to be done beforehand.
• You need to define the quality assurance process and which staff to be involved (possibly QA staff)
• The annotation manager needs to address the collaboration between the annotators. Who will help who? Who will cross-check whose work?
• You divide the project into smaller phases, then give feedbacks to erroneous work.
• The Annotation manager will be the one who ensures technical support for the annotation tool throughout the annotation process to prevent project delay. If there is to be any problem that can’t be solved singlehandedly, he/she needs to ask the tool provider or the project manager for viable solutions.

 

4. Feedback and changes

After the annotation is complete, it is important to assess the overall outcome and how the team did the work. By doing this, you can confirm the validity and reliability of the annotations before submitting them to another team or clients.

If there were any additional annotations, you need to take another look at the strategic adjustments to the project’s definition, training process, and workforce, so the next round of annotation collection can be more efficient.

It is also very important to implement processes to detect data drift and anomalies that may require additional annotations.

 

How Lotus QA manager our annotation projects

To ensure the high quality on your training datasets is not easy. Actually, it is quite a troublesome process to allocate the work, do the training and give feedback. Maintaining such a large team of the project manager, annotation manager and annotators can take up many resources and effort.

 

 

LQA is one of the top 10 Data labelling companies in Vietnam with a team of 6-year experience, working in multiple annotation projects and many data types. We also have a strong team of data annotation project managers and QA staff to ensure the quality of our outputs. From agriculture to fashion, from sports to automobile projects, we’ve done it all. Working with LQA, you can rest assured that your data is in the right hand. Don’t hesitate to contact us if you want to know more about managing data annotation projects.

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Fanpage: https://www.facebook.com/LotusQualityAssurance

 

The matter of Data Labeling Quality has been a major topic of concern in AI/ML communities. Perhaps the most common “principle” that you might come across solving this puzzle is “Garbage in, garbage out”.

By saying this, we want to emphasize the fundamental law with training data for artificial intelligence and machine learning development projects. Poor-quality training datasets fed to the AI/ML model can lead to numerous errors in operation.

For example, training data for autonomous vehicles is the deciding factor for whether the vehicles can function on the roads. Provided with poor-quality training data, the AI model can easily mistake humans for an object or the other way round. Either way, the poor training datasets can result in high risks of accidents, which is the last thing that autonomous vehicle manufacturers would want in their projects.

For high-quality training data, we need to involve data labeling quality assurance in the data processing procedure. At Lotus Group and Lotus QA, we take the three following actions to ensure high-quality training datasets. Take a look at this fundamental guide to provide your AI/ML model with the best training data.

Don’t know where to start in AI data processing? Check out our Data Annotation Guide.

 

1. Clarify requirements to optimize data labeling quality

The precision of annotations

High data labeling quality doesn’t simply mean the most carefully annotated data or the training data of the highest quality. For strategic data annotation projects, we need to clarify the requirements of the training datasets. The questions that annotation team leaders should answer are how high-quality the data needs to be, the acceptable precision of data annotation, and how detailed the output should be.

As a vendor of data annotation quality, one thing that we always ask our clients is the requirements. “How tedious do you want us to work with the datasets?”, “How would you want the precision of our annotations?”. By answering these questions, you will have ahead of you a benchmark for your entire projects later on.

 

 

Skillful levels of the annotators

Keep in mind that the implementations of Artificial Intelligence and Machine Learning are very broad. Besides the common applications in autonomous vehicles and transportation, AI and ML made their debut in healthcare and medical, agriculture, fashion, etc. For each and every industry, there are hundreds of different projects, working on different kinds of objects, hence different skills and knowledge are required to ensure data annotation quality.

Take road annotation vs. medical data annotation for example.

• For roads annotation, the work is quite straightforward, and you only need annotators who are capable of common knowledge to do the work. For this annotation project, the number of datasets that need annotating can add up to millions of videos or pictures, and the annotators have to keep the productivity high in an acceptable level of quality.
• Medical data requires annotators who work in the medical field with particular knowledge. For the case of diabetic retinopathy, trained doctors are asked to grade the severity of diabetic retinopathy from photographs so that deep learning can be applied in this particular field.

 

 

Even for well-trained doctors, not all of their annotations agree with one another. To have a consistent outcome, one annotation team might have to annotate each file multiple times to eventually come to a correlation.

It is a matter of how complicated the given data is and how detailed the clients want the data output to be. Once these things are clarified, the team leader can work on the allocation of resources for the required outcomes. Metrics and the relevant Quality Assurance process will be defined after this.

Example of an ideal output

We also encourage the clients to provide example sets to act as the “benchmark” for every dataset to be annotated. This is the most straightforward technique for the quality assurance of data annotation that one might employ. With the example of the perfectly annotated data, your annotators now are trained and presented to the baseline of their work.

With the benchmark as the ideal outcome, you can calculate agreement metrics to evaluate each annotator’s accuracy and performance. In case of uncertainty in both the annotation and review process, the QA staff can work with these sample datasets to define which are qualified and which are not.

 

2. Multi-layered QA process

The QA process in data labeling projects varies within different companies. At LQA, we adhere to the international standardized quality assurance process. The predetermined preferences will always be clarified right at the beginning of the project. These preferences will be compiled into one “benchmark” which will, later on, act as the “golden standard” of every label and annotation.

The steps of this multi-layered QA process are: Self-check, Cross-check, and Manager-check.

 

Self-check

In this step, annotators are asked to do the review on their own work. With self-assessment, annotators now have the time to look back at the data annotation tool, annotation, and labeling from the start of the project.

Normally, annotators have to work under great pressure in terms of time and workload, which can possibly lead to potential deviations in their work. The quality assurance starting with the self-check step will be the time for annotators to slow down and take a thorough look at how they’ve done. By acknowledging the mistakes and possible deviations, annotators can fix them themselves and avoid any of those in the future.

 

 

Cross-check

In data science in general and data annotation in particular, you might have heard about the term “bias”. Annotation bias refers to the situation in which annotators have their own habit to label the data, which can lead to biased opinions upon the provided data. In some cases, annotator bias can influence the model performance. For a more robust AI and ML model, we have to take some effective measures to eliminate the biased annotations, and one simple way to do this is to cross-check.

 

 

By carrying out cross-checking in your annotation process, the whole work is viewed differently, hence the annotators can identify the mistakes and errors in their colleagues’ work. Again, with this different view, the reviewer can point out the biased annotations and the team leader can take further actions. They can rework or give another round of assessment to see whether the annotations are really biased.

 

Manager’s review

An annotation project manager is usually responsible for the day-to-day oversight of the annotation project. Their main tasks include selecting/managing the workforce and ensuring data quality and consistency.
The manager will be the one that receives the data sampling from clients and work on the required metrics and carries out training for the annotators. Once the cross-checking is done, the manager can randomly check the output to see whether they adhere to the clients’ requirements.

Prior to all these checks, the annotation project manager also has to draw a “benchmark line” for quality assurance. To ensure annotation consistency and accuracy, any work that is under the predefined quality must be reworked.

 

 

 

3. Quality Assurance staff involvement

Data labeling quality control cannot rely only on the annotation team. In fact, the involvement of professional and experienced quality assurance staff is a must. To ensure the highest quality of your annotation work, a team of quality assurance staff is a must. They will work as an independent department, outside of the annotation team, and not under the management of the annotation project manager.

The ideal percentage of quality staff over the entire number of data annotation staff doesn’t go beyond 10%. The QA staff cannot and will not review every single annotated data in your project. In fact, they will randomly take out datasets and once again, review the annotations.

 

 

These QA staffs are well-trained with the data sample and will have their metrics to evaluate the quality of the annotated data. These metrics must be agreed upon between the QA team leader and the annotation project manager beforehand.

In addition to the three-step of review of self-check, cross-check and manager’s review, the involvement of QA staff in your annotation projects will sure adhere your data output to the predefined benchmark, which eventually ensures the highest level possible for your training data.

Want to hear more from professionals to enhance your data labeling quality? Contact LQA for more information:

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Fanpage: https://www.facebook.com/LotusQualityAssurance

Vietnam is amongst the top destinations for AI data processing services, providing top-notch data labeling, data collecting and data annotation work. With many favorable traits that can help businesses reduce costs as much as possible, we now have a whole ecosystem of the top Data Labeling companies in Vietnam.

If you are looking for a reliable AI data processing service provider in Vietnam, you can consider our list of top 10 data annotation companies.

You might want to know: Why is Auto Data Labeling the future?

 

Overview of data labeling companies in Vietnam

The demands for AI data processing services hit a record-high number as the world’s technology is revolving around AI-related technologies. To operate an AI model, one business might need thousands of training datasets. The increasing need for AI development and training data leads to the increasing needs for data collection, data annotation and data validation.

Since the dawn of AI and ML, there have been hundreds of companies founded just to handle data processing services (because the number needed is very high). The most mature market in this particular field is the US and China. However, as these countries move further towards AI development, the cost for operating an AI data processing hub gets higher and higher. In these countries, the workforce once dedicated to AI processing services now switch to other AI-related technologies.

To maintain a reliable and stable source of training datasets, AI development companies have to come to other countries for a better cost, and Vietnam is one of the most reasonably-priced destinations.

In Vietnam, the price for hiring and retaining talents is lower than that of China or the US. We also have a young and abundant workforce that can cover your needs for training data.

Our AI data processing services started to boom 6 years ago. And in only 6 years, a whole new ecosystem of the most prestigious and renowned AI data annotation companies are founded and still operating with great prospects:

• Lotus Quality Assurance
• DIGI-TEXX VIETNAM
• Sibai
• SANEI HYTECHS VIETNAM Co., Ltd.
• BEETSOFT Co., Ltd
• MP.BPO
• Vietnam Smart BPO (VSBPO)
• Kotwel
• OkLabel
• Vie-Partner

 

 

Details about top data labeling companies in Vietnam

Top data labeling companies in Vietnam can provide you with an array of different services to fulfill your needs in AI development and AI data processing.

 

Lotus Quality Assurance

Lotus Quality Assurance, as part of Lotus Group, was founded in 2016 with the start of a Testing and Quality Assurance company. As the company moves towards the newest technologies there are in the market, our BOD has come to the realization that AI data processing service holds great potential and prospects for further development. Indeed, since its foundation, Lotus QA has continuously worked with international clients in different data annotation, data annotation and data validation projects. Besides project-based work, Lotus QA has been a long-term partner of multiple clients, mostly in the automotive sector.

 

 

Especially, our annotators and QA engineers assure high-quality training data and annotated data with an average error rate of only 0.02%, which is very ideal for any annotation project.

Since the foundation of Lotus QA, data annotation has always been the key service offering for our clients. As we thrive in this area, we have been working with many kinds of data, ranging from image, text, voice from different sectors. These sectors are automotive, agriculture, construction, fashion, finance, etc.

 

DIGI-TEXX VIETNAM

DIGI-TEXX is a German IT- BPO company headquartered in Ho Chi Minh City, Vietnam since 2002, with 3 branches in Ho Chi Minh City and one office in Fukuoka, Japan. With 100% FDI from Germany, DIGI- TEXX is one of the pioneers in the Business Processing Outsourcing (BPO) industry in Vietnam. As a digital solution provider with a solid BPO background, we empower clients around the world from various industries to achieve business transformation and gain competitive advantages.

With more than 1000 employees, providing round-the-clock services, they guarantee service delivery excellence while ensuring compliance with industry-followed quality and security standards.

They have been consistently providing Outsourced Services and Digital Solutions for more than 19 years to international clients in various industries, that require:

• Document processing to save time and optimize cost.
• Digital solutions to replace paperwork with automation processes, such as Banking, Insurance, and Healthcare.

Besides, they also provide Customer Helpdesk services in fluent Vietnamese, Chinese, Japanese, and English for many E-commerce and trading platforms.

 

SIBAI VIETNAM

SIBAI VIETNAM was founded in 2020 with a dedicated team of more than 200 experienced annotators who can handle your most unstructured datasets. With competent staff who have worked on multiple projects, SIBAI VIETNAM can now carry out your data annotation project on multiple platforms with different data annotation tool, across all content types.

With the combination of human talents and AI, SIBAI VIETNAM thrives as one of the most successful data labeling companies in Vietnam. Our customers’ most complex labeling needs can be well handled and addressed.

 

 

 

With high-quality data labeling and data annotation services, SIBAI is to elevate your business growth. SIBAI VIETNAM has developed a talent pool of more than 200 well-trained annotators in diverse areas. With all combined, we can provide the most suitable solutions that you are looking for, anytime you need them.

Besides the usual data annotation service, SIBAI VIETNAM also focuses on content moderation solutions. SIBAI provides human-level accuracy that significantly moderates community-generated threats in image, video, text, and audio. SIBAI can help brands limit risk exposure and safeguard their online platforms from content that has been flagged as inappropriate or violating community guidelines.

 

SANEI HYTECHS VIETNAM Co., Ltd.

Established on 19th June 2015, SANEI HYTECHS VIETNAM Co., Ltd. is currently one of the best data labeling companies in Vietnam. With the association with Japanese branches and companies, Sanei has strong resources and a foundation for top-notch services. Their service offering includes:

• Software Development (Embedded software, third-party unit verification, software application on Windows, Android, iOS and Bluetooth, etc.)
• LSI Design (FPGA Design/Verification, Logic Dedsign/Verification), Ip Design/Verification)
• Annotation Center (Create, analyze and provide design/evaluation data toward Big Data processing, deep learning data creation of the Artificial intelligence development, BPO service)

SANEI HYTECHS VIETNAM Co., Ltd. is currently operating with small number of employees but it can stretch in scale if requested.

 

 

 

BEETSOFT Co., Ltd

Beetsoft is another stand-out name among the data labeling companies in Vietnam. With more than 5 years of experience working in IT Consultancy and outsourcing services, Beetsoft knows how to play a stellar role in honing the skills of professionals, assisting companies to achieve success in their operating fields. Based in Vietnam and Japan, Beetsoft focuses on providing services to these two markets. Especially in the data labeling and data annotation fields, Beetsoft stands out as it can provide high-quality projects thanks to international standards and a multi-layered QA system.

Beetsoft offers high-end services at competitive rates as our development and annotator team is based in Vietnam. The competitive price of Beetsoft is always accompanied by the best work there is, so their customers can rest assured of the quality.

 

MP.BPO

BPO.MP Co., Ltd. is the first BPO enterprise with the Vietnam-Japan joint venture model to provide Business Process Outsourcing services, including document digitization, data entry & processing data management, financial and accounting processing, content writing, translation-interpretation, image processing, document labeling, etc.

With the motto “Successful cooperation to overcome limits”, the company’s development goal is to combine the advantages of the two cultures of Vietnam – Japan, take advantage of the strengths of businesses of the two countries to provide the best services. MP.BPO promises to bring services of international quality for customers in Vietnam and around the world.

 

Vietnam Smart BPO (VSBPO)

Vietnam Smart BPO (VSBPO) is a brand under Free’t Planning Vietnam, a joint venture between Vietnam, Free’t Planning Japan and I-Corporation Japan. VSBPO takes pride in being a pioneer in the industry, and a leader in providing business process outsourcing (BPO) services in Vietnam. Their partner, Free’t Planning Japan, has 20+ years of experience in IT & BPO industries. Today, the total number of employees is 200+ across 3 countries (Japan, Vietnam, China).

With the vision of becoming the leading BPO company in Vietnam, VSBPO is to provide the best quality services at optimal cost to clients.

 

Kotwel

Kotwel is the emerging data service provider for artificial intelligence. Relying on its own data resources, technical advantages and rich data processing experience, since its establishment, Kotwel has provided high-quality data services to many technology companies and scientific research institutions worldwide.

 

 

Kotwel is committed to total customer satisfaction by providing consistently high-quality data & services that meet or exceed the expectations of our worldwide customers.

Their purpose remains to embrace the power of human ingenuity and technology to create value for your AI & Business Initiatives. Kotwel wants to enable enterprises globally with stellar quality data services by using the combination of advanced tools and human intelligence. Benefitting and creating an optimistic social change through employment.

By supporting the development of game-changing AI & Technology applications with cutting edge workforce solutions, Kotwel wants to become a global leader when it comes to solving your data needs.

 

Ikorn Solutions

As a leader in contemporary online trends, Ikorn Solutions has grown as a highly respected IT company and become a trusted partner of many large Korean firms since entering the IT outsourcing market in 2007. They specialize in software development and I.T. outsourcing services such as data labeling services that are comprehensive, integrated, and customized to suit individual business needs across industries.

Driven by a passion for technology, Ikorn strongly believes that quality integration and technological development are at the center of their business. Ikorn´s competitive advantages are a force to be proud of as an excellent pool of skilled resources recruited from the finest professional education institutions in the industry. In 2017, following 10 years of operation and great persistence in development, Ikorn Solutions took a consistent and rigorous approach to expand our outsourcing services into the automotive industry and began to seek new partners for the next phase of business. This move served to affirm, step by step, the company’s strong position in the software technology market.

 

Vie-Partner

In 2016, VP Studio was founded by a team of computer graphics artists, providing graphic and 2D/3D designs for movies and games productions.

After observing the similarities of working methods and logic between Computer Graphics and Data Annotation, they found that experienced graphic designers achieve a 30% higher annotation speed and accuracy than average.

With years of experience in graphics training, they founded Vie-Partner specializing in Data Annotation. The goal of Vie-Partner is to provide organizations with trustworthy labeling solutions while creating work chances for underprivileged youngsters in Vietnam, minimalize costs without compromising quality.

 

If you are looking for the high-quality data labeling services in Vietnam, contact Lotus QA for more information from experts:

• Website: https://www.lotus-qa.com/
• Tel: (+84) 24-6660-7474
• Fanpage: https://www.facebook.com/LotusQualityAssurance

The world has witnessed unprecedented growth in the information technology market and IT outsourcing trend, which can be seen in almost every aspect of our daily lives. With its share in the “market pie” remaining with a steady rate prior to the Covid-19 pandemic, the year of 2022 will mark a new milestone in the IT field in general and in IT Outsourcing in particular.

Why IT Outsourcing Trend?

IT Outsourcing is a service that has long been on the market with a relatively steady growth rate. As in the IT Services Outsourcing Market Size, Industry Report, 2020-2027 of Grand View Research, the global IT services outsourcing market is projected to grow at USD 520.74 billion in 2019. The annual growth rate (CAGR) from the phase from 2020 to 2027 is expected to be 7.7%.

Taking advantage of the shifting market

For a minor field in Information Technology, IT Outsourcing Service shows potential, but this growth rate was not that dramatic for us to call IT Outsourcing a flagship point.

However, with the world’s economy brought into a sudden and screeching halt due to the pandemic, many giants in business have shifted their focus to virtual/digital engagement with their clients. 

Surging from the uncharted waters, these businesses have proven the viability and possibility of how digital transformation can save a fortune, or perhaps even bring their names to the top of the chain.

Learning from the big names on the market, many other businesses, from big fish to local store owners, all want to apply the technology advances in their operations. To these businesses, digital transformation is the crossing bridge to bring customers and their services closer, especially under the influence of the pandemic in which people prefer virtual interactions.

Take Amazon and Shopify as examples, we can see that the application of e-commerce platforms was spiking in the first half of 2020. These platforms, of course, aim at selling, while their approach is through applications and software. Amazon, or Shopify, has its own in-house development and QA team. But for mid-sized or small-sized companies, they just can’t afford the HR and operation costs. Under this circumstance, the industry is anticipated to witness substantial demand for IT operations so as to allow companies to focus on their core tasks and reduce operating costs.

Parallel to the core tasks, the marginalized tasks also play an increasingly important role in businesses that are planning to foster digital transformation. 

Since the businesses wanting to employ digital transformation have no foundation or background knowledge over information technology, the IT Outsourcing market is progressing owing to the ever-increasing demand for consultancy.

The talking numbers

As the pandemic continues to put a strain on the global economy, many businesses plan to transition to remote work and online customer engagement and order fulfilment. In order to cope with this new approach, they increase spending on clouds, especially software as a service.

Financial cuts in the circumstance of the pandemic are a must, but the reduction in IT Outsourcing has eased from $83 billion in the spring to $31 billion at the end of 2020, signaling the growth in the global IT spending.

Worldwide IT spending is projected to total $4.5 trillion in 2022, according to Gartner’s forecast, growing by 3% compared to 2021, despite that people tend to cutback spending on PCs, tablets and printers by consumers 

As in the first phases of the pandemic in 2020, every aspect of the IT service was declining, but it began to take the initial steps for a huge growth in the years coming. For example, after contracting 4.6% in 2020 to $490 billion, worldwide IT spending on consulting and implementation services are predicted to experience a 4.5% CAGR through 2024. While worldwide spending on IT-centric managed services, infrastructure, and application support, which decreased 1.1% in 2020 to $475 billion, will see a CAGR of 5.3% through 2024. 

What companies want from their IT outsourcing providers

Pre-pandemic, the main focus for IT outsourcing providers was narrowly on specific services such as helpdesk, infrastructure, storage, network monitoring and network management. 

Post-pandemic, with the preferred solutions for digital transformation stay on top in almost every business, the IT outsourcing services are demanded to leverage and innovate to cope with the urgent needs for a wider range of requirements.

Subsequently, the expected outcome for this whole IT outsourcing service is cost avoidance. To achieve this, IT outsourcing providers are to fulfil the needs for:

1. AI and Automation

The employment of 4.0 Technology is developing with the pace that we’ve never seen before, leading to an upsurge in the need for human resources and infrastructure. Thousands of applications pilot every day, each with many features that require timid, tedious work of coding, testing and maintenance.

To this point, businesses who want to be ahead of the curve have to take advantage of being the pioneer, meaning they have to be the fastest and the most productive. Instead of the traditional way of expanding the team with experts in the field (which can be quite costly), many of the business owners decide to go for a cost-effective approach, AI and automation.

The fascinating idea of AI – a non-human machine that can interact with people is on the rise. But the real benefit of this is to reduce HR and operational costs. For example, before assigning a customer to a human customer service officer, the system has a chatbot to answer and interact with them. Only when the bot cannot figure out the requirement and how to fulfil it do they transfer the customer to a CS. With the bot work regardless of time, the business can save a fortune on the cost for a CS team.

2. Growth of the Cloud Services

On-premise storage for data management has shown weakness and limitations, hence the IT outsourcing trend in shifting to cloud services. 

Alongside the current worth of cloud computing reaching the hallmark of $180 billion worldwide is the market growth by 24% of PaaS, SaaS, and IaaS sections. In two years’ time, cloud computing service is predicted to soar to over $623.3 billion. 

One of the reasons why cloud computing is on the rise is the better protection of data. Moreover, it also ensures faster data operations and the ability to modernize business processes.

3. 5G

5G wireless technology is meant to deliver higher multi-Gbps peak data speeds, ultra low latency, more reliability, massive network capacity, increased availability, and a more uniform user experience to more users. Higher performance and improved efficiency empower new user experiences and connect new industries. – Qualcomm

With the employment of 5G in almost every aspect of the IT world, it speeds up the adoption of reliability, low latency and larger network capacity. Alongside its emerging deployment in major aspects such as medtech or Internet of Things, 5G also plays an important role in the development of AI implementation.

For example, as the Covid-19 pandemic took its toll on the world, some 5G-based applications have already made their way into medtech, especially in the adoption of telehealth and remote monitoring. All of the wireless technology, powered by 5G, have benefited the healthcare staff with utmost convenience.

For the part of AI implementation, 5G is pervasive in domains such as autonomous driving, virtual reality and augmented reality. With higher connection density and the ability to handle an immense number of connected devices at the same time, 5G comes to the forefront as the pioneering factor for both cost avoidance and service enhancement.

4. Cybersecurity

There’s no denying that information technology advances are developing with upsoar rate, resulting in the ever-growing number of service end-users. Larger number of users equals larger threat of cybersecurity. 

To have a screw loose in the cybersecurity is to bring threats to the system, but to recruit a full-stack IT security engineer is no easy task. Instead of having an in-house staff who works full-time, businesses are leaning towards IT outsourcing. They often need:

• Monitor your environment 24/7
• Thorough security staff training
• Security strategy
• Security architecture

One report from Allied Market Research estimates the market to reach nearly $41 billion by 2022, based on a 16.6% compound annual growth rate between 2016 and 2022.

5. Remote Work Statistics

According to Weforum, “The number of days US employees spend working from home increased from 1.58 per week in January 2021 to 2.37 in June 2022”, as the result of Covid-19. The IT sector, among many other sectors, has witnessed the dramatic shift to remote work, marking a new IT Outsourcing trend in the IT outsourcing market.

Working remotely is not new, especially under the specific traits of how IT staff can work. However, the rate is increasing with soaring popularity.

A report by Avasant shows that middle-sized tech companies have been the largest contributors to the growth of the IT outsourcing industry in 2020.

It’s also declared that the average outsourcing for midsize companies went from 9.1% to 11.8%. So while some tech businesses increased their IT budgets on the brink of the pandemic, the rest continued to work with their nearshore and offshore IT outsourcing partners to reduce development costs.

Delve deeper into other technology trends and industry movement.

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Automated Data Labeling is a new feature that is currently being constantly mentioned among Data annotation trends, and some even deem it the solution for the time-consuming and resource-consuming casual manual annotation.

As the Manual Data Labeling – aka Manual Data Annotation takes hours to annotate one dataset, the Automated data labeling technology now proposes a simpler, faster and more advanced way of processing data, through the use of AI itself.

 

How we normally handle dataset

The most common and simplest approach to data labeling is, of course, a fully manual one. A human user is presented with a series of raw, unlabeled data (such as images or videos), and is tasked with labeling it according to a set of rules.

For example, when processing image data for machine learning, the most common types of annotations are classification tags, bounding boxes, polygon segmentation, and key points.

 

 

Classification tags, which are the easiest and cheapest annotation, may take as little as a few seconds whereas fine-grained polygon segmentation could take a few minutes per each instance of objects.

In order to calculate the impact of AI automation on data labeling times, let’s assume that it takes a user 10 seconds to draw a bounding box around an object, and select the object class from a given list.

In this case, provided with a typical dataset with 100,000 images and 5 objects per image, annotators would have to spend 1,500 man-hours to complete the annotation process. This eventually would cost approximately $10,000 just for data labeling. 

The price of $10.000 is only for data labeling. For annotation project managers, AI data processing takes more than that. To ensure the high quality of the training data, they are compelled to add other layers of quality control and quality assurance. This helps manually verify and review each piece of labeled data, but it would be very costly. Moreover, the quality control and quality assurance staff must be trained of the sample output so that they understand what is required in the outcome of the annotation projects, thereby increasing the labeling costs by about 10%.

 

 

Some annotation project managers might choose consensus-based quality control. By implementing this method, the whole annotation project goes through multiple annotations. The same piece of data is annotated multiple times, and the results are consolidated and compared for quality control purposes. With this method, the amount of time and money is proportional to the number of annotators working on the same task. Simply put, if you had three users label the same image three times, you would have to pay for all 3 annotations. 

All this is to emphasize that, the two most expensive steps in data labeling are:

• The data labeling itself
• Reviewing and verifying it for quality control. 

 

Looking at all the huge costs that it would take in an annotation project, many business leaders have turned into a less time-consuming and tedious solution, which is the auto annotation tool technology.

Thankfully, with the latest technologies in artificial intelligence and machine learning, automated data labeling, or auto annotation, is usable now. However, to create an effective and well-rounded auto annotation tool now, it even requires more training data and human input for correcting errors induced by the AI. Therefore, anyone has the naive attempt to entirely apply auto annotation tools, they have to be cognizant of the truth that the tools are not the one-size-fits-all solution.

 

The advantages of Automated Data Labeling

Automated data labeling is quite a new term in the field, but the technology advancement implementing and making it happen is developing with high speed, shown in the large number of tools on the market now. So what are auto data labeling and its benefits?

 

What’s automated data labeling?

Automatic labeling is a feature found in data annotation tools that apply artificial intelligence (AI) to enrich, annotate, or label a dataset. Tools with this feature augment the work of humans in the loop to save time and money on data labeling for machine learning.

 

 

Most tools allow you to load pre-annotated data into the tool. More advanced tools, which are evolving into platforms (e.g., tool plus Software Development Kit or SDK), allow you to leverage AI or bring your own algorithm to the tool to improve the data enrichment process by auto labeling data.

Other tools offer prediction models that suggest annotations so workers can validate them. Some features leverage embedded neural networks that can learn from every annotation made. All of these features can save time and resources for machine learning teams and will have a profound effect on data annotation workflows.

 

Outstanding benefits of automated data labeling

When working with organizations using tools to annotate images for machine learning, we find two optimal ways to apply auto labeling in data annotation workflow:

• Pre-annotate some or all of your dataset. Workers come behind the automation to review, correct, and complete the annotations. Automation cannot annotate everything; there will be exceptions and edge cases. It’s also far from perfect, so you must plan for people to make reviews and corrections as necessary.
• Reduce the amount of work sent to people. An auto-labeling model can assign a confidence level based on the use case, task difficulty, and other factors. It enriches the dataset with annotations, and sends annotations with lower confidence scores to a person for review or correction.

We’ve run time experiments, with one team using tools that have an automation feature versus another team that is manually annotating the same data. In some cases, we’ve seen auto labeling provide low-quality results which increase the amount of time required per annotation task. Other times, it has provided a helpful starting point and reduced task time.

 

 

In one image annotation experiment, auto labeling combined with human-powered review and improvements was 10% faster than the 100% manual labeling process. That time savings increased from 40% to 50% faster as the automation learned over time.

It also had a more than the five-pixel margin of error for vehicles and missed the objects that were farthest from the camera. As you can see in the image, an auto-labeling feature tagged a garbage bin as a person. It’s important to keep in mind that pre-annotation predictions are based on existing models and any misses in the auto labeling reflect the accuracy of those models.

Data annotation tools can include automation, also called auto labeling, such as Labelbox and Tagtog, which uses artificial intelligence to label data, and workers can confirm or correct those labels, saving time in the process.

While auto labeling is not perfect, it can provide a helpful starting point and reduce task time for data labelers.

 

 

Some tasks are ripe for pre-annotation. For example, if you use the example from our experiment, you could use pre-annotation to label images, and a team of data labelers can determine whether to resize or delete the labels, or bounding boxes.

This reduction of labeling time can be helpful for a team that needs to annotate images at pixel-level segmentation.

Our takeaway from the experiments is that applying auto labeling requires creativity. We find that our clients who use it successfully are willing to experiment, fail, and pivot their process as necessary.

As auto data labeling is one of the breakthroughs for a better outlook of the AI technology, specifically machine learning, we still have a lot to discover with this new term.

 

 

If you want to hear from our experts concerning the matter of Automated data labeling, please contact us for further details.

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Parallel to the fast-paced development of the Artificial Intelligence and Machine Learning market, the field of data annotation is moving forward with the most accelerating trends, both in terms of tools and workflow.

From AI-Powered Virtual Assistant to Autonomous Cars, data annotation has played an important role.

Some might think that data annotation is a boring, timid and time-consuming process, while others might deem it the crucial element of artificial intelligence’s success. 

In fact, data annotation, or AI data processing, was once the most-unwanted process of implementing AI in real life. However, with the ever-growing expansion AI in multiple fields of our daily lives, the needs for rich, versatile and high-quality datasets are higher than ever. 

In order for a machine to run, in this case, is the AI system, we have to pour training data in so that the “machine” could learn to adapt to whichever is coming at it.

With these trends in the data annotation and AI data processing market, it not only sets a new outlook for the whole market, it also proves the urgent needs for well-annotated datasets.

 

Predictive Annotation Tools – Auto Labeling Tool

It is pretty obvious that the more fields we can apply Artificial Intelligence and Machine Learning in, the more we need AI data processing. 

By saying AI data process, we also mean that we need both the data collection and data annotation.

The rapidly expanding needs of the AI and machine learning market have set a new goal for another focus of the data annotation process. As it is with the Testing market, the demands for auto labeling, or we can call it predictive annotation tools are coming to a peak.

 

Basically, the predictive annotation tools (auto labeling tools) are the tools that can automatically detect and label items with the foundation of the similar existing manual annotations.

With the implementation of the aforementioned tools, after some manually annotated data, the toolkit can subsequently annotate the similar datasets.
Throughout this process, the human intervention is limited to the minimum amount, hence saving a lot of time and effort to do such repetitive and boring tasks.

With just some scratches on the surface, auto labeling, or predictive annotation tools may be the pivotal change that will boost up the speed of the annotation process by 80%. But to put one auto labeling tool on the market, it takes years of developing sophisticated features, not to mention a large number of data types need to be put in the data annotation system of that tool. That is why you often see one tool for only one data type.

While the advantages of an auto labeling tool are undeniable, the cost for one commercial tool like that can be enormous.

 

Emphasis on Quality Control

It is sure that Quality Control plays a huge role in every process. However, the current situation only shows that QC is only circumstantial. 

In the future, data engagements at scale will be the main focus, requiring a higher emphasis on quality control.

With more data labeling solutions going into production, and later into the training model of AI systems, more edge cases will be considered.

 

Under this circumstance, it is a must that you build your own teams of QC to exclusively handle the quality of the annotated datasets. They will not work the way the old QC staff did. On the contrary, these specialized experts can function without detailed guidelines and focus on spotting and fixing issues with large datasets.

What about security? With the security, the QC team should follow a stringent process of maintaining security of the annotation process. This should be ensured throughout the whole project.

 

Involvement of metadata in data annotation process

From autonomous vehicles to medical imaging, in order for the AI system to run smoothly without glitches, a staggering amount of data is required for annotation.

Metadata is the data clarifying your data. With the same old annotations as the code snippets you put in at the Java class or method level that further define data about the given code without changing any of the actual coded logic, metadata is for data management.

 

All in all, metadata is created and collected for the better utility of that data.

If we can make good use of the metadata, any human errors including misplacing things, management malfunctions, etc. will be tackled. With metadata in hand, we will be able to find, use, preserve and reuse data in a more systematic manner.

• In finding data, metadata speeds up the process of finding the relevant information. Take a dataset in the form of audio for example. Without metadata and the management from it, it would be impossible to us to find the location of the data. This also applies to data types such as images and videos.
• In using the data, metadata gives us a better understanding of how the data is structured, definitions of the terms, its origin (where it was collected, etc.)
• In re-using data, metadata helps annotators navigate the data. In order to reuse data, annotators are to have careful reservation and documentation of the metadata.

The key to making all of this happen is data annotation. Adding metadata to datasets helps detect patterns and annotation helps models recognize objects.

With all the benefits of metadata in how we can manage and use the datasets, many firms now have grown interested in developing metadata for better management.

 

Workforce of SMEs

The rapidly growing number of the industries embracing AI, a subject-specific data annotation team is of urgent needs. 

For every domain such as healthcare, finance, automotive, etc. a team trained with custom curricular will be deployed on projects, hence expert annotators built over time. With this being done, more value and high-quality to the annotation process will be focused with a deeper approach, and this strategy will start with the validation of guidelines to time of data delivery.

 

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The AI-Powered Virtual Assistant Market Size is estimated to be at $3.442 Billion in 2019, and this number is expected to surpass $45.1 Billion by 2027, raising by 37.7% (according to a study by CAGR). And this can all start from the simple voice annotation.

The possibility and utility of AI-Powered Virtual Assistants come from both technical and behavioral aspects. In correlation with the ever-growing demand for on-app assistance, we have the data inputs continuously poured into the AI system for data training. 

To put it another way, one of the most important features to make AI-powered virtual assistants possible is the data inputs, aka voice annotation.

 

The booming industry of AI and virtual assistant

For starters, an intelligent virtual assistant (IVA), or we can call it an AI-powered virtual assistant, is a software technology that is developed to provide responses similar to those of a human. 

With this assistant, we can ask questions, make arrangements or even demand actual human support.

 

Why are virtual assistants on the rise?

Intelligent virtual assistants are widely used, mostly for the reduced cost of customer handling. Also, with quick responses for live chat or any other form of customer engagement, IVA helps boost customer service satisfaction and save time.

Besides external performance as above, IVA also collects customer information and analyzes conversation & customer satisfaction survey responses; thereby, helping organizations improve the customer and company communication.

 

Intelligent virtual assistants can play as the avatars of the enterprises. They can dynamically read, understand and respond to queries from customers, and eventually reduce costs for manpower in different departments. 

We can see many of those IVAs in large enterprises as they can help eliminate the infrastructure setup cost. This is why the revenue for IVA are so high in recent years and perhaps in the years coming.

 

What can virtual assistants do?

The usability and adoption of AI-powered virtual assistance are everywhere. We can see it in our operating systems, mobile applications or even chatbots. With the deployment of machine learning, deep neural networks and other advancements in AI technology, the virtual assistant can easily perform some certain tasks.

 

Virtual assistants are very common in operating systems. These assistants help in setting calendar, making arrangements, setting alarms, asking questions or even writing texts. A multitasking assistant like this is on the large scale, and we might think that these applications are limited within  operating systems only.

However, with the soaring numbers of mobile users and mobile apps, many entrepreneurs and even start-ups are beginning to implement a virtual assistant just within their product apps. This leads to the rising demand for the data input required in different fields.

For example, a healthcare service app requires specific voice annotations regarding medical terms and other healthcare-related matters.

In the report of ResearchAndMarkets.com concerning Global Intelligent Virtual Assistant (IVA) Market 2019-2025: Industry Size, Share & Trends, it is indicated that:

• Smart speakers are developing with the fastest pace and emerging as the major domain for IVA
• Still, Text to speech is the largest segment in IVA. It is estimated to reach a revenue of over $15.37 Billion by 2025
• The country with the dominance in the market of IVA is North America with the main industry of healthcare.
• The key players are Apple Inc., Oracle Corporation, CSS Corporation, WellTok Inc., CodeBaby Corporation, eGain Corporation, MedRespond, Microsoft, Next IT Corporation, Nuance Communications, Inc., and True Image Interactive Inc.

Through the report, we can see that the potential to develop and grow the AI-powered virtual assistant market is on fast-paced growth. For every different domain, we have a different approach for the implementation of IVA.

For better service and business development, enterprises demand effective customer engagement, hence the growing number of virtual assistants to be implemented in different products.

Currently, the intelligent virtual assistant market is majorly driven by the BFSI industry vertical, owing to its higher adoption and increasing IT investment. However, automotive & healthcare are the most lucrative vertical segments and are likely to maintain this trend during the forecast period.

 

How can voice annotation help the IVA?

As Virtual Assistant appears in almost every aspect of life, including calling, shopping, music streaming, consulting, etc., the requirement for voice data processing continues to grow. Besides the speech to text and text to speech annotation, more advanced forms of part of speech tagging or phonetics annotation are also in high demand.

 

For a IVA system to operate properly, the developer has to consider different approaches of interaction methods, including:

• Text-to-text: Text-to-text annotation is not necessarily directly related to the operation of IVA. Nevertheless, labeled texts help the machine understand the natural language of humans. If not done properly, the annotated texts can lead a machine to exhibit grammatical errors or wrongly understand the queries from customers. 
• Speech-to-text: Speech-to-text annotation transcribes audio files into text, usually in a word processor to enable editing and search. Voice-enabled assistants like Siri, Alexa, or Google Assistant are fine examples for this.
• Text-to-speech: Text-to-speech annotation enables the machine to synthesize natural-sounding speech with a wide range of voice (male, female) and accents (Northern, Middle and Southern accent). 
• Speech-to-speech: Speech-to-speech is the most advanced and complicated form of annotation. With the data input of this, the AI can understand the speech of users, and then answer/perform accordingly.

Whichever of the above, we still have to collect data, voices, speeches, conversations, and then annotate them so that machine learning algorithms can understand the input from users.

Voice annotation service requires much effort to deliver understandable and useful datasets. It also takes much time to even recruit and train the annotators, not to mention the on-job time.

If you want to outsource voice annotation, contact LQA now for instant support.

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One of the most popular use cases of AI and Data Annotation is Autonomous Car. The idea of Autonomous Cars (or Self-Driving Cars) has always been a fascinating field for exploitation, even in entertainment or actual transportation. 

This was once just a fictional outlook, but with the evolution of information technology and the technical knowledge obtained over the years, autonomous cars are now possible.

 

Perhaps the most famous implementation of AI and Data Annotation in Autonomous Cars is Tesla Autopilot, which enables your car to steer, accelerate and brake automatically within its lane under your active supervision, assisting with the most burdensome parts of driving. 

However, Tesla Autopilot has only been confirmed of success in several Western countries. The real question here is that: “Can Tesla Autopilot be used in highly congested roads of South-East Asia countries?”

 

The role of Data Annotation in AI-Powered Autonomous Cars

Artificial Intelligence (AI) is the leading trend of Industry 4.0, there’s no denying that. Big words and the “visionary” outlook of AI in everyday life are really fascinating, but the actual implementation of this is often overlooked. 

In fact, the beginning of AI implementation started off years ago with the foundation of a virtual assistant, which we often see in fictional blockbuster movies. In these movies, the world is dominated by machines and automation. Especially, vehicles such as cars, ships and planes are well taken care of thanks to an AI-Powered Controlling System.

With the innovation of multiple aspects of AI Development, many of the above have become true, including the success in Autonomous/Self-Driving Cars.

 

Training data with high accuracy

The two important features of a self-driving car are hardware and software. For an autonomous car to function properly, it is required to sense the surrounding environment and navigate objects without human intervention.

The hardware keeps the car running on the roads. Besides, the hardware of an autonomous car also contains cameras, heat sensors or anything else that could detect the presence of objects/humans.

The software is perhaps the standing point of this, in which it has machine learning algorithms that have been trained. 

 

Labeled datasets play an important role as the data input for the aforementioned learning algorithms. Once annotated, these datasets will enrich the “learning ability” of AI software, hence improving the adaptability of the vehicles.

 

With high accuracy of the labeled datasets, the algorithm’s performance will be better. The poor-performing data annotation can lead to possible errors during a driving experience, which can be really dangerous.

 

Enhanced Experience for End-users

Who wouldn’t pay for the top-notch experience? Take Tesla as your example. Tesla models are the standard, the benchmark that people unconsciously set for other autonomous vehicle brands. From their designs to how the Autopilot handles self-driving experience, they are combined to create a sense of not only class but also safety.

How Tesla designs their cars is a different story. What really matters for the sake of their customers is safety.

Leaving everything for “the machine” might be frightening at first, but Tesla also guarantees that through many of the experiments and versions of the AI software. In fact, it was proven that Tesla Autopilot can easily run on highway roads of multiple Western countries.

 

We might have seen the footage of how Tesla Autopilot Model X was defeated on the highly congested roads of Vietnam. However, we have to take a look back at the scenario in which we need an autonomous car the most. 

The answer here is the freeway and highway. And Tesla can do very well on these roads.

The role of data annotation in this case is that through the high-quality annotated datasets, the machine is trained with high frequency, therefore securing safety for passengers.

 

The future of autonomous vehicles

We don’t simply jump from No Driving Automation to Full Driving Automation. In fact, we are barely at Level 3, which is Conditional Driving Automation.

• Level 0 (No Driving Automation): The vehicles are manually controlled. Some features are designed to “pop up” automatically whenever problems occur.
• Level 1 (Driver Assistance): The vehicles feature single automated systems for driver assistance, such as steering or accelerating (cruise control). 
• Level 2: (Partial Driving Automation): The vehicles support ADAS (steering and accelerating). Here the automation falls short of self-driving because a human sits in the driver’s seat and can take control of the car at any time. 
• Level 3 (Conditional Driving Automation): The vehicles have “environmental detection” capabilities and can make informed decisions for themselves, such as accelerating past a slow-moving vehicle. But they still require human override. The driver must remain alert and ready to take control if the system is unable to execute the task. Tesla Autopilot is qualified as Level 3.
• Level 4 (High Driving Automation): The vehicles can operate in self-driving mode within a limited area.
• Level 5 (Full Driving Automation): The vehicles do not require human attention. There’s no steering wheel or acceleration/braking pedal. We are far from Level 5.

With Tesla Autopilot qualified as Level 3, we are only halfway through the journey to the full driving automation.

However, we personally think that the matter of these Level 3 vehicles is the training data for the AI system. The datasets that have been poured into this are very limited, possibly can be compared to just a drop in the ocean.

 

To train the AI system is no easy task, as the datasets require not only accuracy but also high quality, not to mention the enormous amount of them.

The speed in which Tesla or any other autonomous vehicle company is going for is quite high in order to be ahead of the competition. Instead of doing it themselves, these companies often seek help at some outsourcing vendor for better management and execution of data processing. These vendors can help with both data collecting and data annotating.

Want to join the autonomous market without worrying about data annotation? Get consults from LQA to come up with the best-fitted data annotation tool for your business. Contact us now for full support from experts.

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In this dynamic era of machine learning, the fuel that powers accurate algorithms and AI breakthroughs is high-quality data. To help you demystify the crucial role of data annotation for machine learning, and master the complete process of data annotation from its foundational principles to advanced techniques, we’ve created this comprehensive guide. Let’s dive in and enhance your machine-learning journey.

Data Annotation for Machine Learning

What is Machine Learning?

Machine learning is embedded in AI and allows machines to perform specific tasks through training. With data AI annotation, it can learn about pretty much everything. Machine learning techniques can be described into four types: Unsupervised learning, Semi-Supervised Learning, Supervised Learning, and Reinforcement learning

• Supervised Learning: Supervised learning learns from a set of labeled data. It is an algorithm that predicts the outcome of new data based on previously known labeled data.
• Unsupervised Learning: In unsupervised machine learning, training is based on unlabeled data. In this algorithm, you don’t know the outcome or the label of the input data.
• Semi-Supervised Learning: The AI will learn from a dataset that is partly labeled. This is the combination of the two types above.
• Reinforcement Learning: Reinforcement learning is the algorithm that helps a system determine its behavior to maximize its benefits. Currently, it is mainly applied to Game Theory, where algorithms need to determine the next move to achieve the highest score.

Although there are four types of techniques, the most frequently used are unsupervised and supervised learning. You can see how unsupervised and supervised learning works according to Booz Allen Hamilton’s description in this picture:

What is Annotated Data?

Data annotation for machine learning is the process of labeling or tagging data to make it understandable and usable for machine learning algorithms. This involves adding metadata, such as categories, tags, or attributes, to raw data, making it easier for algorithms to recognize patterns and learn from the data.

In fact, data annotation, or AI data processing, was once the most unwanted process of implementing AI in real life. Data annotation AI is a crucial step in creating supervised machine-learning models where the algorithm learns from labeled examples to make predictions or classifications.

The Importance of Data Annotation Machine Learning

Data annotation plays a pivotal role in machine learning for several reasons:

• Training Supervised Models: Most machine learning algorithms, especially supervised learning models, require labeled data to learn patterns and make predictions. Without accurate annotations, models cannot generalize well to new, unseen data.
• Quality and Performance: The quality of annotations directly impacts the quality and performance of machine learning models. Inaccurate or inconsistent annotations can lead to incorrect predictions and reduced model effectiveness.
• Algorithm Learning: Data annotation provides the algorithm with labeled examples, helping it understand the relationships between input data and the desired output. This enables the algorithm to learn and generalize from these examples.
• Feature Extraction: Annotations can also involve marking specific features within the data, aiding the algorithm in understanding relevant patterns and relationships.
• Benchmarking and Evaluation: Labeled datasets allow for benchmarking and evaluating the performance of different algorithms or models on standardized tasks.
• Domain Adaptation: Annotations can help adapt models to specific domains or tasks by providing tailored labeled data.
• Research and Development: In research and experimental settings, annotated data serves as a foundation for exploring new algorithms, techniques, and ideas.
• Industry Applications: Data annotation is essential in various industries, including healthcare (medical image analysis), autonomous vehicles (object detection), finance (fraud detection), and more.

Overall, data annotation is a critical step in the machine-learning pipeline that facilitates the creation of accurate, effective, and reliable models capable of performing a wide range of tasks across different domains.

How to Process Data Annotation for Machine Learning?

Step 1: Data Collection

Data collection is the process of gathering and measuring information from countless different sources. To use the data we collect to develop practical artificial intelligence (AI) and machine learning solutions, it must be collected and stored in a way that makes sense for the business problem at hand.

There are several ways to find data. In classification algorithm cases, it is possible to rely on class names to form keywords and to use crawling data from the Internet to find images. Or you can find photos, and videos from social networking sites, satellite images on Google, free collected data from public cameras or cars (Waymo, Tesla), and even you can buy data from third parties (notice the accuracy of data). Some of the standard datasets can be found on free websites like Common Objects in Context (COCO), ImageNet, and Google’s Open Images.

Some common data types are Image, Video, Text, Audio, and 3D sensor data.

• Image data annotation for machine learning (photographs of people, objects, animals, etc.)

Image is perhaps the most common data type in the field of data annotation for machine learning. Since it deals with the most basic type of data there is, it plays an important part in a wide range of applications, namely robotic visions, facial recognition, or any kind of application that has to interpret images.

From the raw datasets provided from multiple sources, it is vital for these to be tagged with metadata that contains identifiers, captions, or keywords.

The significant fields that require enormous effort for data annotation for machine learning are healthcare applications (as in our case study of blood-cell annotation), and autonomous vehicles (as in our case study of traffic lights and sign annotation). With the effective and accurate annotation of images, AI applications can work flawlessly with no intervention from humans.

To train these solutions, metadata must be assigned to the images in the form of identifiers, captions, or keywords. From computer vision systems used by self-driving vehicles and machines that pick and sort produce to healthcare software applications that auto-identify medical conditions, there are many use cases that require high volumes of annotated images. Image annotation increases precision and accuracy by effectively training these systems.

• Video data annotation for machine learning (Recorded tape from CCTV or camera, usually divided into scenes)

When compared with images, video is a more complex form of data that demands a bigger effort to annotate correctly. To put it simply, a video consists of different frames which can be understood as pictures. For example, a one-minute video can have thousands of frames, and to annotate this video, one must invest a lot of time.

One outstanding feature of video annotation in the Artificial Intelligence and Machine Learning model is that it offers great insight into how an object moves and its direction.

A video can also inform whether the object is partially obstructed or not while image annotation is limited to this.

• Text data annotation for machine learning: Different types of documents include numbers and words and they can be in multiple languages.

Algorithms use large amounts of annotated data to train AI models, which is part of a larger data labeling workflow. During the annotation process, a metadata tag is used to mark up the characteristics of a dataset. With text annotation, that data includes tags that highlight criteria such as keywords, phrases, or sentences. In certain applications, text annotation can also include tagging various sentiments in text, such as “angry” or “sarcastic” to teach the machine how to recognize human intent or emotion behind words.

The annotated data, known as training data, is what the machine processes. The goal? Help the machine understand the natural language of humans. This procedure, combined with data pre-processing and annotation, is known as natural language processing, or NLP.

• Audio data annotation for machine learning: They are sound records from people having dissimilar demographics.

As the market is trending with Voice AI Data Annotation for machine learning, LTS Group provides top-notch service in annotating voice data. We have annotators fluent in languages.

All types of sounds recorded as audio files can be annotated with additional keynotes and suitable metadata. The Cogito annotation team is capable of exploring the audio features and annotating the corpus with intelligent audio information. Each word in the audio is carefully listened to by the annotators in order to recognize the speech correctly with our sound annotation service.

The speech in an audio file contains different words and sentences that are meant for the listeners. Making such phrases in the audio files recognizable to machines is possible, by using a special data labeling technique while annotating the audio. In NLP or NLU, machine algorithms for speech recognition need audio linguistic annotation to recognize such audio.

Audio data annotation facilitates various real-life AI applications. A prime example is the application of an AI-powered audio transcription tool that swiftly generates accurate transcripts for podcast episodes within minutes. 

• 3D Sensor data annotation for machine learning: 3D models generated by sensor devices.

No matter what, money is always a factor. 3D-capable sensors greatly vary in build complexity and accordingly – in price, ranging from hundreds to thousands of dollars. Choosing them over the standard camera setup is not cheap, especially given that you would usually need multiple units in order to guarantee a large enough field of view.

 

Low-resolution data annotation for machine learning

In many cases, the data gathered by 3D sensors are nowhere as dense or high-resolution as the one from conventional cameras. In the case of LiDARs, a standard sensor discretizes the vertical space in lines (the number of lines varies), each having a couple of hundred detection points. This produces approximately 1000 times fewer data points than what is contained in a standard HD picture. Furthermore, the further away the object is located, the fewer samples land on it, due to the conical shape of the laser beams’ spread. Thus the difficulty of detecting objects increases exponentially with their distance from the sensor.”

Step 2: Problem Identification

Knowing what problem you are dealing with will help you to decide the techniques you should use with the input data. In computer vision, there are some tasks such as:

• Image classification: Collect and classify the input data by assigning a class label to an image.
• Object detection & localization: Detect and locate the presence of objects in an image and indicate their location with a bounding box, point, line, or polyline.
• Object instance / semantic segmentation: In semantic segmentation, you have to label each pixel with a class of objects (Car, Person, Dog, etc.) and non-objects (Water, Sky, Road, etc.). Polygon and masking tools can be used for object semantic segmentation.

 

Step 3: Data Annotation for Machine Learning

After identifying the problems, now you can process the data labeling accordingly. With the classification task, the labels are the keywords used during finding and crawling data. For instance segmentation task, there should be a label for each pixel of the image. After getting the label, you need to use tools to perform image annotation (i.e. to set labels and metadata for images). The popular annotated data tools can be named Comma Coloring, Annotorious, and LabelMe.

However, this way is manual and time-consuming. A faster alternative is to use algorithms like Polygon-RNN ++ or Deep Extreme Cut. Polygon-RNN ++ takes the object in the image as the input and gives the output as polygon points surrounding the object to create segments, thus making it more convenient to label. The working principle of Deep Extreme Cut is similar to Polygon-RNN ++ but it allows up to 4 polygons.

It is also possible to use the “Transfer Learning” method to label data, by using pre-trained models on large-scale datasets such as ImageNet, and Open Images. Since the pre-trained models have learned many features from millions of different images, their accuracy is fairly high. Based on these models, you can find and label each object in the image. It should be noted that these pre-trained models must be similar to the collected dataset to perform feature extraction or fine-turning.

Types of Annotation Data

Data Annotation for machine learning is the process of labeling the training data sets, which can be images, videos, or audio. Needless to say, AI Annotation is of paramount importance to Machine Learning (ML), as ML algorithms need (quality) annotated data to process.

In our AI training projects, we use different types of annotation. Choosing what type(s) to use mainly depends on what kind of data and annotation tools you are working on.

• Bounding Box: As you can guess, the target object will be framed by a rectangular box. The data labeled using bounding boxes are used in various industries, mostly in automotive vehicle, security, and e-commerce industries.
• Polygon: When it comes to irregular shapes like human bodies, logos, or street signs, to have a more precise outcome, Polygons should be your choice. The boundaries drawn around the objects can give an exact idea about the shape and size, which can help the machine make better predictions.
• Polyline: Polylines usually serve as a solution to reduce the weakness of bounding boxes, which usually contain unnecessary space. It is mainly used to annotate lanes on road images.
• 3D Cuboids: The 3D Cuboids are utilized to measure the volume of objects which can be vehicles, buildings, or furniture.
• Segmentation: Segmentation is similar to polygons but more complicated. While polygons just choose some objects of interest, with segmentation, layers of alike objects are labeled until every pixel of the picture is done, which leads to better results of detection.
• Landmark: Landmark annotation comes in handy for facial and emotional recognition, human pose estimation, and body detection. The applications using data labeled by landmarks can indicate the density of the target object within a specific scene.

Popular Tools of Data Annotation for Machine Learning

In machine learning, data processing, and analysis are extremely important, so I will introduce to you some Tools for annotating data to make the job simpler:

• Labelbox: Labelbox is a widely used platform that supports various data types, such as images, text, and videos. It offers a user-friendly interface, project management features, collaboration tools, and integration with machine learning pipelines.
• Amazon SageMaker Ground Truth: Provided by Amazon Web Services, SageMaker Ground Truth combines human annotation and automated labeling using machine learning. It’s suitable for a range of data types and can be seamlessly integrated into AWS workflows.
• Supervisely: Supervised focuses on computer vision tasks like object detection and image segmentation. It offers pre-built labeling interfaces, collaboration features, and integration with popular deep-learning frameworks.
• VGG Image Annotator (VIA): Developed by the University of Oxford’s Visual Geometry Group, VIA is an open-source tool for image annotation. It’s commonly used for object detection and annotation tasks and supports various annotation types.
• CVAT (Computer Vision Annotation Tool): CVAT is another popular open-source tool, specifically designed for annotating images and videos in the context of computer vision tasks. It provides a collaborative platform for creating bounding boxes, polygons, and more.

When selecting a data annotation for machine learning tool, consider factors like the type of data you’re working with, the complexity of annotation tasks, collaboration requirements, integration with your machine learning workflow, and budget constraints. It’s also a good idea to try out a few tools to determine which one best suits your specific needs.

it is crucial for businesses to consider the top 5 annotation tool features to find the most suitable one for their products: Dataset management, Annotation Methods, Data Quality Control, Workforce Management, and Security.

Who can annotate data?

The data annotators are the ones in charge of labeling the data. There are some ways to allocate them:

In-house Annotating Data

The data scientists and AI researchers in your team are the ones who label data. The advantages of this way are easy to manage and has a high accuracy rate. However, it is such a waste of human resources since data scientists will have to spend much time and effort on a manual, repetitive task.

In fact, many AI projects have failed and been shut down, due to the poor quality of training data and inefficient management.

In order to ensure data labeling quality, you can check out our comprehensive Data annotation best practices. This guide follows the steps in a data annotation project and how to successfully and effectively manage the project:

• Define and plan the annotation project
• Managing timelines
• Creating guidelines and training workforce
• Feedback and changes

Outsourced AI Annotations Data

You can find a third party – a company that provides data annotation services. Although this option will cost less time and effort for your team, you need to ensure that the company commits to providing transparent and accurate data. 

Online Workforce Resources for Data Annotation

Alternatively, you can use online workforce resources like Amazon Mechanical Turk or Crowdflower. These platforms recruit online workers around the world to do data annotation. However, the accuracy and the organization of the dataset are the issues that you need to consider when purchasing this service.

 

The Bottom Line

The data annotation for machine learning guide described here is basic and straightforward. To build machine learning, besides data scientists who will set the infrastructure and scale for complex machine learning tasks, you still need to find data annotators to label the input data. Lotus Quality Assurance provides professional data annotation services in different domains. With our quality review process, we commit to bringing a high-quality and secure service. Contact us for further support!

 

Our Clients Also Ask

 

Outsourcing the data labeling services to emerging BPO destinations like Vietnam, China, and India has become a recent trend. However, it is not easy to choose the most suitable data labeling outsourcing vendor among numerous companies. In this article, LQA will walk you through some advices to find the best vendor.

 

1. Prepare a clear project requirement

 

First of all, it is crucial to prepare a clear and detailed requirement which shows all of your expectations toward the final results. You should include the project overview, timeline and budget in your request. A good requirements should include:

– What data types annotators have to work with?
– What kind of annotations need to be done?
– Is it required to have expertise knowledge to label your data?
– The dataset need to be annotated with how much accuracy rate?
– How many files need to be annotated?
– What is the deadline for your project?
– How much can you spend on this project?

 

2. Must-have Criteria to Evaluate the vendors

 

After finalizing your requirements, you should evaluate the vendors with whom you will sign the contract. This stage is crucial since you don’t want to spend plenty of money to receive a poor-labored dataset. We suggest evaluating them based on their experience, quality, efficiency, security, and teammate.

 

Experience

 

While data labeling may often seem like a simple task, it does require great attention to detail and a special set of skills to execute efficiently and accurately on a large scale. You need to gain a solid understanding of how long each vendor has been working specifically in the data annotation space and how much experience their annotators have. To evaluate this, you can ask the vendor some questions about their years of experience, the domain they have worked with, and the annotation types. For example:

How many years of experience in data annotation do the vendors have?
Did they work with a project that requires special domain knowledge before?
Do the vendors provide the type of annotation that matches your requirements?

 

Quality

 

The data scientists often define the quality in datasets for model training by how precisely the labels are placed. However, it is not about labeling correctly one or two times, but it requires consistently accurate labeling. You can figure out the capability of providing high-quality labeled data of the vendors by checking:

The error rates of their previous annotation projects
How accurately placed were the labels
How often did the annotator properly tag each label?

 

Data Quality – 5 Essentials of AI Training Data Labeling Work

 

Efficiency

 

Annotation is more time-consuming than you imagine. For example, a 5-minute video will have an average of 24 frames in one sentence, which made up to 7200 images that need to be labeled. The longer time annotators spend labeling one image, the more hour required to complete the task. To estimate correctly how many man-hours requested to complete your project, you should check with the vendor:

How long did it take to place each label on average?
How long did it take to label each file on average?
How long did it take to execute quality checking on each file?

 

Team

 

Understanding the ability of your vendor annotation team is important as they are the ones who directly execute the project. The vendor should commit to providing you a well-trained team. Moreover, if you want to label text, you need to check if the labeling team can speak the language or not. Besides, confirm with your vendors whether they are ready to scale up or down the annotation team in a short period. Although you may estimate the amount of data to be labeled, your project size still can change over time.

 

Data Annotators: The secret weapon of AI development

 

 

3. Require a pilot project

 

A pilot project is an initial small-scale implementation that is used to prove the viability of a project idea. It enables you to manage the risk of a new project and analyze any deficiencies before substantial resources are committed.

If you ask the vendor to do a pilot project, you will need to choose some sample data from your dataset. You can start with a small amount containing various types of data (10-15 files, depending on the complexity of your dataset).

Remember to provide a detailed guideline for the demo so you can evaluate the vendor correctly. Last but not least, ask them how you can check the progress of the demo test. As a result, you can rate if their quality and performance tracking tools or processes satisfy your requirement or not.

 

We went along with all the set up you need to notice before signing any contract with a data labeling outsourcing vendor. Hopping that with this preparation, you can choose the most decent partner.

If you are shortlisting data labeling vendors, why don’t you include LQA in the list? We have many experiences of labeling data in various fields like healthcare, automotive, and e-commerce. Contact our experts to know more about our experience and previous projects.