Data Labeling for Machine Learning: Guide + Tools

Data labeling is crucial for training accurate machine learning models. Here's what you need to know:

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• Definition: Adding tags/annotations to raw data (images, text, audio, etc.)
• Purpose: Helps ML models understand and learn from data
• Importance: Directly impacts model accuracy and performance

Key aspects of data labeling:

Aspect Description Types Text, images, audio, video, sensor data Common tasks Classification, object detection, sentiment analysis Methods In-house, outsourcing, crowdsourcing, automated Challenges Consistency, cost, bias, quality control Tools Annotorious, LabelMe, Labelbox, Stanford CoreNLP

Best practices:

• Create clear labeling guidelines
• Implement quality checks
• Use appropriate tools for your data type
• Consider advanced methods like active learning

By following this guide, you'll be equipped to effectively label data for your ML projects.

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Basics of Data Labeling

Types of Data to Label

Data labeling involves adding tags to different kinds of data:

Data Type Examples Common Labeling Tasks Text Documents, emails, social media posts Sentiment analysis, Named Entity Recognition Images Photos, diagrams, maps Image segmentation, classification Audio Speech, music, sound effects Speaker identification, genre classification Video Movies, TV shows, surveillance footage Action recognition, object tracking Sensor Data IoT device outputs Pattern recognition, anomaly detection

Common Data Labeling Tasks

Different projects need different labeling tasks:

1. Image Classification: Tagging whole images
2. Object Detection: Finding and boxing objects in images
3. Semantic Segmentation: Separating image objects from backgrounds
4. Pose Estimation: Marking body points to show posture
5. Sentiment Analysis: Sorting text by mood (positive, negative, neutral)
6. Named Entity Recognition: Picking out names of people, places, etc. in text

Problems in Data Labeling

Data labeling can face several issues:

Problem Description Solution Inconsistency Different labelers tag things differently Use clear rules and check work often Time and Cost Labeling takes a lot of time and money Use efficient tools and some automation Expert Knowledge Some projects need special know-how Get help from experts in the field Bias Labelers might add unintended bias Use diverse labelers and bias-checking tools Quality Control Keeping labels good across big datasets is hard Do regular checks and validate labels Data Security Keeping data safe when others label it Use secure systems and make people sign agreements

Solving these problems helps create good datasets for accurate machine learning models.

How to Label Data: Step-by-Step

Getting Your Data Ready

Before labeling, prepare your data:

• Gather diverse data to reduce bias
• Ensure data represents real-world scenarios
• Example: For self-driving cars, collect images from various angles and conditions

Picking a Labeling Method

Choose a method that fits your project:

Method Good Points Bad Points In-house Better control, expert knowledge Uses more resources Outsourcing Can handle large amounts, cost-effective Might have quality issues Crowdsourcing Quick, cheap Less control over quality Computer-generated Creates extra data Needs powerful computers Automated Good for big datasets May need human checks

Writing Clear Labeling Rules

Make a clear guide for labelers:

• Show right and wrong label examples
• Explain how to handle tricky cases
• Use pictures to show labeling methods
• List specific rules for each label type

Setting Up Quality Checks

Keep your labeled data good:

1. Set up regular checks
2. Do random and targeted reviews
3. Use multiple labelers for important tasks
4. Set goals to measure labeler work

Carrying Out the Labeling

Label data well by:

• Training labelers thoroughly
• Using good labeling tools
• Setting up a smooth labeling process
• Keeping open lines for questions

Checking and Fixing Labels

Keep improving your labeled data:

1. Look at random samples often
2. Use methods to check label accuracy
3. Give feedback to labelers regularly
4. Keep fixing errors as you find them

Ways to Label Data

There are several ways to label data. Each has its good and bad points. Here's a look at the main methods:

Method What It Is Good Points Bad Points In-house Team Using your own staff Better control, meets specific needs Takes more time, costs more Online Workers Using platforms like Amazon Mechanical Turk Cheaper, faster Needs careful management Outside Help Hiring specialized companies or freelancers Can be cost-effective, access to special tools Less direct control Computer-Made Data Creating fake data with algorithms Useful for adding to existing data Needs lots of computing power Semi-Automatic Mix of human labelers and computer tools More efficient than manual only Needs careful setup

Using Your Own Team

This means your data scientists and engineers do the labeling. It gives you more control but can be slow and expensive for big projects.

Using Online Workers

This method uses websites where many people can do small tasks. It's often cheaper and faster than using your own team, but you need to watch the quality closely.

Hiring Outside Help

You can pay other companies or freelancers to do the labeling. This can save money and give you access to experts, but you have less direct control.

Using Computer-Made Data

This involves making new data with computer programs. It's good for adding to what you already have, but it needs powerful computers and people who know how to use them.

Semi-Automatic Labeling

This combines people and computer tools. It can be faster than just using people, but you need to set it up carefully to make sure it works well.

The best way to label your data depends on what your project needs.

Tips for Good Data Labeling

Here are some tips to help you label data well for machine learning:

Keeping Labels the Same

Make sure all labelers use the same labels and rules. Create a clear guide that shows:

• What each label means
• How to use labels correctly
• Examples of right and wrong labeling

Dealing with Unusual Cases

Unusual data can be hard to label. Here's how to handle it:

Approach Description Create a new category Make a special label for odd cases Ask experts Get help from people who know the subject well Document decisions Write down how you chose to label tricky items

Handling Unclear Data

When data is hard to understand:

1. Try to get more info from where the data came from
2. Ask other labelers what they think
3. Use computer tools to help figure it out
4. Mark it as "unsure" if you can't decide

Always Improving Your Process

Keep making your labeling better:

• Ask labelers how to make the job easier
• Check label quality often
• Fix problems as soon as you find them
• Update your labeling guide when needed

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Here are some tools for labeling images and videos:

Tool Type Key Features Annotorious Free, open-source Web-based, allows text comments and drawings LabelMe Online Helps build image databases, has mobile app Sloth Free Works with images and videos, has face recognition

For labeling text data, try these tools:

Tool Type Key Features Labelbox Paid Basic labeling, custom interfaces Stanford CoreNLP Free Integrated NLP toolkit Bella Open-source GUI, database for managing labeled data

To label audio data, consider these options:

Tool Type Key Features Dataturks Paid Training data preparation tools Tagtog Web-based Text and audio annotation

Comparing Popular Labeling Platforms

When picking a data labeling tool, look at these factors:

Factor What to Consider Cost Free vs. paid options Customization Can you change the tool to fit your needs? Integration Does it work with your current tools? Support Is help available when you need it?

Choose a tool that fits your project's needs and budget.

Advanced Data Labeling Methods

Active Learning

Active learning mixes human know-how with machine learning to make data labeling better. Here's how it works:

1. Train a model on a small set of labeled data 2. Use this model to pick important data for humans to label 3. Repeat until the model is good enough

This method helps focus on key data points and saves time and money.

Semi-Supervised Learning

Semi-supervised learning uses both labeled and unlabeled data to train models. It's useful when you don't have much labeled data.

Step Description 1 Train on small labeled dataset 2 Use model to label unlabeled data 3 Add newly labeled data to training set 4 Retrain model and repeat

This approach can make models better with less labeled data.

Using Pre-Trained Models for Labeling

Pre-trained models can speed up data labeling. These models have learned from big datasets already.

Benefits of using pre-trained models:

• Save time and effort
• Label data faster
• Can be adjusted for specific tasks
• Work well for quick labeling needs

To use pre-trained models:

1. Pick a model that fits your task
2. Fine-tune it with some of your data
3. Use it to label new data

This method can make data labeling quicker and more accurate.

Checking and Improving Label Quality

Ways to Measure Label Quality

To make sure your labels are good, you need to check them often. Here are some ways to do this:

Method Description Track key numbers Look at how accurate, fast, and error-free your labeling is Regular checks Keep an eye on these numbers over time Team meetings Talk with your labelers to make sure everyone is doing things the same way

Getting Different Labelers to Agree

It's important that all your labelers give the same labels to the same things. Here's how to make that happen:

Approach How it works Use multiple labelers Have more than one person label each item Compare answers Check if different labelers gave the same labels Clear instructions Give easy-to-follow rules for labeling Fair labeling Teach labelers to be neutral and not favor any groups

Avoiding Bias in Labeling

Keeping bias out of your labels helps your machine learning work better for everyone. Try these methods:

Step What to do Set clear rules Write down exactly how to label things Train your team Teach labelers about bias and how to avoid it Mix up your labelers Use people from different backgrounds Keep checking Look at labels often to spot any bias Get outside help Ask others to review your work

You can also clean up your data before and after labeling:

• Before: Fix any problems in the raw data
• After: Adjust your model to be more fair

Labeling More Data

Handling Big Datasets

When working with large amounts of data, try these methods:

Method Description Split into chunks Break big datasets into smaller parts Use computer tools Speed up labeling with automated systems Mix people and machines Have humans check computer-labeled data

These steps can make big labeling jobs easier to manage.

Using Computers to Help Label

Computer tools can speed up labeling for large datasets. They work well for:

• Image sorting
• Speech recognition
• Text grouping

But remember:

• Computers can make mistakes
• People should check computer work
• Some data types need human labeling

Leading a Team of Labelers

To guide a labeling team well:

1. Give clear instructions

• Write easy-to-follow rules
• Show examples of good and bad labels

2. Check work often

• Look at random samples
• Fix common mistakes

3. Talk with your team

• Have regular meetings
• Ask for feedback on the labeling process

4. Train and support

• Teach new skills
• Help with tough labeling choices

Good leadership helps teams make better labels faster.

Wrap-Up

Main Points to Remember

Data labeling is key for machine learning projects. It means adding tags to data so computers can learn from it. Good data labeling needs:

• Clear rules
• Good training
• Regular checks
• Tools for working together

The most important things are:

Aspect Why It Matters Consistency All labels should mean the same thing Accuracy Labels must be correct Reliability You can trust the labels

What's Next for Data Labeling

As machine learning grows, data labeling will become more important. New computer tools will make labeling faster and better. Here's what to expect:

Future Trend What It Means AI-assisted labeling Computers help people label data Better guidelines Clear rules everyone can follow Quality measures Ways to check if labels are good

FAQs

What is applying labels to training data with known targets?

Data labeling is adding tags to data so computers can learn from it. It's a key step in getting data ready for machine learning.

Here's what data labeling does:

Purpose Description Add tags Put labels on data items Show targets Tell the computer what to predict Help learning Let the machine learn from examples

Data labeling is important because:

• It helps machines learn the right things
• Good labels make the computer's guesses more correct
• The quality of labels affects how well the machine works

When you label data:

1. You look at each piece of data
2. You decide what tag it should have
3. You add that tag to the data

This process helps machines learn to make good guesses about new data they haven't seen before.

Related posts

What is data labeling and how does it work? Read this comprehensive guide to learn the common types and best practices of data labeling.

Worldwide dialogues on artificial intelligence and machine learning typically evolve around two things &#; data and algorithms. To stay on top of the dynamically uphill tech, you want to be aware of both.

If we were to describe their correlation briefly, AI models use algorithms to learn from what is called training data and then apply that knowledge to meet the model objectives. For the purposes of this article, we will focus on data only.

What is data labeling?

Data labeling is a stage in machine learning that aims to identify objects in raw data (such as images, video, audio, or text) and tag them with labels that help the machine learning model make accurate predictions and estimations. Now, identifying objects in raw data sounds all sweet and easy in theory. In practice, it is more about using the right annotation tools to outline objects of interest extremely carefully, leaving as little room for error as possible. That for a dataset of thousands of items.

Contact us to discuss your requirements of Automatic Labeling Machine Learning. Our experienced sales team can help you identify the options that best suit your needs.

Though raw data itself does not mean much to a supervised model, poorly labeled data could cause your model to go down in flames.

In this post, we&#;ll cover everything you need to know about data labeling to make informed decisions for your business and ultimately develop high-performance AI and machine learning models:

• Why use data labeling?
• How does data labeling work?
• Common types of data labeling
• What are some of the best practices for data labeling?
• What should I look for when choosing a data labeling platform?

Why use data labeling?

Labeled datasets are especially pivotal to supervised learning models, where they help a model to really process and understand the input data. Once the patterns in data are analyzed, the predictions either match the objective of your model or don&#;t. And this is where you define whether your model needs further tuning and testing.

Data annotation, when fed into the model and applied for training, can help autonomous vehicles stop at pedestrian crossings, digital assistants recognize voices, security cameras detect suspicious behavior, and so much more. If you want to learn more about use cases for labeling, check out our post on the real-life use cases of image annotation.

How does data labeling work?

In the meantime, here's a walkthrough of specific steps involved in the data labeling process:

Data collection

It all starts with getting the right amount and variety of data that suffice with your model requirements. And there are several ways you could go here:

Manual data collection:

A large and diverse amount of data guarantees more accurate results compared to a small amount of data. One real-world example is Tesla collecting large amounts of data from its vehicle owners. Though using a human resource for data assembly is not technically feasible for all use cases.

For instance, if you&#;re developing an NLP model and need reviews of multiple products from multiple channels/sources as data samples, it might take you days to find and access the information you need. In this case, it will make more sense to use a web scraping tool, which can help in automatically finding, collecting, and updating the information for you.

Open-source datasets:

An alternative option is using open-source datasets. The latter can enable you to perform training and data analysis at scale. Accessibility and cost-effectiveness are among the two primary reasons why specialists may opt for open-source datasets. Besides, incorporating an open-source dataset is a great way for smaller companies to really capitalize on what is already in reserve for large-sized organizations.

With this in mind, beware that with open-source, your data can be prone to vulnerability: there&#;s the risk of the incorrect use of data or potential gaps that will affect your model performance in the end result. So, it all comes down to identifying the value open-source brings to your model and calculating tradeoffs to undertake the ready-made dataset.

Synthetic data generation:

Synthetic data/datasets are both a blessing and a curse, as they can be controlled in simulated environments by creators. And they are not as costly as they may seem at the outset. The primary costs associated with synthetic data are the initial simulation expenses for the most part. Synthetic datasets are commonplace across two broad categories, computer vision and tabular data (e.q., healthcare and security data). Autonomous driving companies often happen to be at the forefront of synthetic data generation consumption, as they come to deal with invisible or occluded objects more often. Hence, the need for a faster way to recreate data featuring objects that real-life scenario datasets miss.

Other advantages of using open-source datasets include boundless scalability and the cover-up for edge cases, where the manual collection would be dangerous (given the possibility of always generating more data vs. aggregating manually).

Data tagging

Once you have your raw (unlabeled) data up and ready, it&#;s time to give your objects a tag. Data tagging consists of human labelers identifying elements in unlabeled data using a data labeling platform. They can be asked to determine whether an image contains a person or not or to track a ball in a video. And for all these tasks, the end result serves as a training dataset for your model.

Now, at this point, you&#;re probably having concerns about your data security. And indeed, security is a major concern, especially if you&#;re dealing with a sensitive project. To address your deepest concerns about safety, SuperAnnotate complies with industry regulations.

Bonus: With SuperAnnotate, you&#;re keeping your data on-premise, which provides greater control and privacy, as no sensitive information is shared with third parties. You can connect our platform with any data source, allowing multiple people to collaborate and create the most accurate annotations in no time. You can also whitelist IP addresses, adding extra protection to your dataset. Learn how to set it up.

Quality assurance

Your labeled data must be informative and accurate to create top-performing machine learning models. So, having a quality assurance (QA) in place to check the accuracy of your labeled data goes a long way. By improving the instruction flow for the QA, you can significantly improve the QA efficiency, eliminating any possible ambiguity in the data labeling process.

Some of the things to keep in mind is that locations and cultures matter when it comes to perceiving objects/text that is subject to annotation. So, if you have a remote international team of annotators, make sure they&#;ve undergone proper training to establish consistency in contextualizing and understanding project guidelines.

QA training can end up being a long-term investment and pay off in the long run. Though training only might not ensure consistent quality in delivery for all use cases. That&#;s where live QA steps to the fore, as it helps detect and prevent potential errors right on the spot and level up productivity levels for data labeling tasks.

Model training

To train an ML model, you have to feed the machine learning algorithm with labeled data that contains the correct answer. With your newly trained model, you can make accurate predictions on a new set of data. However, there are a number of questions to ask yourself before and after training to provide prediction/output accuracy:

1) Do I have enough data?

2) Do I get the expected outcomes?

3) How do I monitor and evaluate the model&#;s performance?

4) What is the ground truth?

5) How do I know if the model misses anything?

6) How do I find these cases?

7) Should I use active learning to find better samples?

8) Which ones should I pick out to label again?

9) How do I decide if the model is successful in the end?

Rule of thumb: It&#;s not enough to deploy your model in production. You also have to keep an eye on how it&#;s performing. There&#;s a wonderful resource that we put together to further guide you on how to build not just a training dataset but premium quality SuperData for your AI. Make sure to check it out.

Common types of data labeling

We suggest viewing data labeling through the lens of three major categories:

Large language models (LLMs)

Large language models (LLMs) have recently become the talk of the tech town. Famous models like GPT, Mixtral, Grok, DBRX have all passed the data labeling process that usually requires extensive recourses.

Data labeling is the cornerstone of training such language models, enabling them to understand and generate human language in its full complexity. Labeling involves tagging raw data with relevant labels to provide the models with insights into the text's context, intent, and semantics. This groundwork enables the models to generate coherent, contextually appropriate, and meaningful responses.

Carrying out such detailed annotation requires a dedicated team of data trainers and an expert workforce.

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These professionals play a pivotal role in ensuring that the data fed into LLMs is accurately labeled, offering the nuanced understanding necessary for the AI to learn effectively. The training process begins with collecting a broad and representative dataset, which is then cleaned and formatted during pre-processing.

Afterward, the expert team labels the data, feeding the model with the knowledge to grasp language nuances and contextual cues. This all leads to the final step, where the model goes through deep learning training. It learns to spot patterns and make smart guesses based on all the detailed labels it's been given.

Computer vision

By using high-quality training data (such as image, video, lidar, and DICOM) and covering intersections of machine learning and AI, computer vision models cover a wide range of tasks. That includes object detection, image classification, face recognition, visual relationship detection, instance and semantic segmentation, and much more.

However, data labeling for computer vision has its own nuances when compared to that of NLP. The common differences between data labeling for computer vision vs. NLP mostly pertain to the applied annotation techniques. In computer vision applications, for example, you will encounter polygons, polylines, semantic and instance segmentation, which are not typical for NLP.

Natural language processing (NLP)

Now, NLP is where computational linguistics, machine learning, and deep learning meet to easily extract insights from textual data. Data labeling for NLP is a bit different in that here, you&#;re either adding a tag to the file or using bounding boxes to outline the part of the text you intend to label (you can typically annotate files in pdf, txt, html formats). There are different approaches to data labeling for NLP, often broken down into syntactic and semantic groups. More on that in our post on natural language processing techniques and use cases.

What are some of the best practices for data labeling?

There&#;s no one-size-fits-all approach. From our experience, we recommend these tried and tested data labeling practices to run a successful project.

Collect diverse data

You want your data to be as diverse as possible to minimize dataset bias. Suppose you want to train a model for autonomous vehicles. If the training data was collected in a city, then the car will have trouble navigating in the mountains. Or take another case; your model simply won&#;t detect obstacles at night if your training data was collected during the day. For this reason, make sure you get images and videos from different angles and lighting conditions.

Depending on the characteristics of your data, you can prevent bias in different ways. So, if you&#;re collecting data for natural language processing, you may happen to be dealing with assessment and measurement, which in turn can introduce bias. For instance, you cannot attribute a higher possibility of heinous crime commitment to minority group representatives just by taking the number of arrest rates within their population. So, eliminating bias from your collected data right off is a critical pre-processing step that precedes data annotation.

Collect specific/representative data

Feeding the model with the exact information it needs to operate successfully is a game-changer. Your collected data has to be as specific as you want your prediction results to be. Now, you may counter this entire section by questioning the context of what we call &#;specific data&#;. To clear things up, if you&#;re training a model for a robot waiter, use data that was collected in restaurants. Feeding the model with training data collected in a mall, airport, or hospital will cause unnecessary confusion.

Set up an annotation guideline

In today&#;s cut-throat AI and machine learning environment, composing informative, clear, and concise annotation guidelines pays off more than you can possibly expect. Annotation instructions indeed help avoid potential mistakes throughout data labeling before they affect the training data.

Bonus tip: How to improve annotation instructions further? Consider illustrating the labels with examples: visuals help annotators, and QAs understand the annotation requirements better than written explanations. The guideline should also include the end goal to show the workforce the bigger picture and motivate them to strive for perfection.

Establish a QA process

Integrate a QA method into your project pipeline to assess the quality of the labels and guarantee successful project results. There are a few ways you can do that:

• Audit tasks: Include &#;audit&#; tasks among regular tasks to test the human laborer's work quality. &#;Audit&#; tasks should not differ from other work items to avoid bias.
• Targeted QA: Prioritize work items that contain disagreements between annotators for review.
• Random QA: Regularly check a random sample of work items for each annotator to test the quality of their work.

Apply these methods and use the findings to improve your guidelines or train your annotators.

Find the most suitable annotation pipeline

Implement an annotation pipeline that fits your project needs to maximize efficiency and minimize delivery time. For example, you can set the most popular label at the top of the list so that annotators don&#;t waste time trying to find it. You can also set up an annotation workflow at SuperAnnotate to define the annotation steps and automate the class and tool selection process.

Keep communication open

Keeping in touch with managed data labeling teams can be tough. Especially if the team is remote, there is more room for miscommunication or keeping important stakeholders out of the loop. Productivity and project efficiency will come with establishing a solid and easy-to-use line of communication with the workforce. Set up regular meetings and create group channels to exchange critical insights in minutes.

Provide regular feedback

Communicate annotation errors in labeled data with your workforce for a more streamlined QA process. Regular feedback helps them get a better understanding of the guidelines and ultimately deliver high-quality data labeling. Make sure your feedback is consistent with the provided annotation guidelines. If you encounter an error that was not clarified in the guideline, consider updating it and communicating the change with the team.

Run a pilot project

Always test the waters before jumping in. Put your workforce, annotation guidelines, and project processes to test by running a pilot project. This will help you determine the completion time, evaluate the performance of your labelers and QAs, and improve your guidelines and processes before starting your project. Once your pilot is complete, use performance results to set up reasonable targets for the workforce as your project progresses.

Note: Task complexity is a huge indicator of whether or not you should run a pilot project. Though oftentimes, complex projects benefit more from a pilot project as you get to measure the success of your project on a budget. Run a free pilot project with SuperAnnotate and get to label data 10x faster.

What should I look for when choosing a data labeling platform?

High-quality data requires an expert data labeling team paired with robust tooling. You can either buy the platform, build it yourself if you can&#;t find one that suits your use case, or alternatively make use of data labeling services. So, what should you look for when choosing a platform for your data labeling project?

Inclusive tools

Before looking for a data labeling platform, think about the tools that fit your use case. Maybe you need the polygon tool to label cars or perhaps a rotating bounding box to label containers. Make sure the platform you choose contains the tools you need to create the highest quality labels.

Think about a couple of steps ahead and consider the labeling tools you might need in the future, too. Why invest time and resources in a labeling platform that you won&#;t be able to use for future projects? Training employees on a new platform costs time and money, so being a couple of steps ahead will save you a headache.

Integrated management system

Effective management is the building block of a successful data labeling project. For this reason, the selected data labeling platform should contain an integrated management system to manage projects, data, and users. A robust data labeling platform should also enable project managers to track project progress and user productivity, communicate with annotators regarding mislabeled data, implement an annotation workflow, review and edit labels, and monitor quality assurance.

Powerful project management features contribute to the delivery of just as powerful prediction results. Some of the typical features of successful project management systems include advanced filtering and real-time analytics that you should be mindful of when selecting a platform.

Quality assurance process

The accuracy of your data determines the quality of your machine learning model. Make sure that the labeling platform you choose features a quality assurance process that lets the project manager control the quality of the labeled data. Note that in addition to a sturdy quality assurance system, the data annotation services that you choose should be trained, vetted, and professionally managed to help you achieve top performance.

Guaranteed privacy and security

The privacy of your data should be your utmost priority. Choose a secure labeling platform that you can trust with your data. If your data is extremely niche-specific, request a workforce that knows how to handle your project needs, eliminating concerns for mislabeling or leakage. It&#;s also a good idea to check out the security standards and regulations your platform of interest complies with. Other questions to ask for guaranteed security include but are not limited to:

1) How is data access controlled?

2) How are passwords and credentials stored on the platform?

3) Where is the data hosted on the platform?

Technical support and documentation

Ensure the data annotation platform you choose provides technical support through complete and updated documentation and an active support team to guide you throughout the data labeling process. Technical issues may arise, and you want the support team to be available to address the issues to minimize disruption. Consider asking the support team how they provide troubleshooting assistance before subscribing to the platform.

Key takeaways

AI is revolutionizing the way we do things, and your business should get on board as soon as possible. The endless possibilities of AI are making industries smarter: from agriculture to medicine, sports, and more. Data annotation is the first step toward innovation. Now that you know what data labeling is, how it works, its best practices, and what to look for when choosing a data annotation platform, you can make informed decisions for your business and take your operations to the next level.

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