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. 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:
- What is data labeling?
- What is it used for?
- 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?
- Key takeaways
What is data labeling?
Data labeling is the task of identifying objects in raw data, such as image, video, text, or lidar, and tagging them with labels that help your 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.
What is it used for?
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:
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.
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).
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.
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.
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 two major categories:
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?
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.
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.