Everything You Need to Know About Data Labeling – Featuring Meeta DashArtificial intelligence (AI) is only as good as the data it is trained with. With the quality and quantity of training data directly determining the success of an AI algorithm, it’s no surprise that, on average, 80% of the time spent on an AI project is wrangling training data, including data labeling. When building an AI model, you’ll start with a massive amount of unlabeled data. Labeling that data is an integral step in data preparation and preprocessing for building AI. But precisely what is data labeling in the context of machine learning (ML)? It’s the process of detecting and tagging data samples, which is especially important when it comes to supervised learning in ML. Supervised learning occurs when both data inputs and outputs are labeled to enrich future learning of an AI model. The entire data labeling workflow often includes data annotation, tagging, classification, moderation, and processing. You’ll need to have a comprehensive process in place to convert unlabeled data into the necessary training data to teach your AI models which patterns to recognize to produce a desired outcome. For example, training data for a facial recognition model may require tagging images of faces with specific features, such as eyes, nose, and mouth. Alternatively, if your model needs to perform sentiment analysis (as in a case where you need to detect whether someone’s tone is sarcastic), you’ll need to label audio files with various inflections.
How to Get Labeled DataData labels must be highly accurate in order to teach your model to make correct predictions. The data labeling process requires several steps to ensure quality and accuracy.
Data Labeling ApproachesIt’s important to select the appropriate data labeling approach for your organization, as this is the step that requires the greatest investment of time and resources. Data labeling can be done using a number of methods (or combination of methods), which include:
- In-house: Use existing staff and resources. While you’ll have more control over the results, this method can be time-consuming and expensive, especially if you need to hire and train annotators from scratch.
- Outsourcing: Hire temporary freelancers to label data. You’ll be able to evaluate the skills of these contractors but will have less control over the workflow organization.
- Crowdsourcing: You may choose instead to crowdsource your data labeling needs using a trusted third-party data partner, an ideal option if you don’t have the resources internally. A data partner can provide expertise throughout the model build process and provide access to a large crowd of contributors who can handle massive amounts of data quickly. Crowdsourcing is ideal for companies that anticipate ramping up toward large-scale deployments.
- By machine: Data labeling can also be done by machine. ML-assisted data labeling should be considered, especially when training data must be prepared at scale. It can also be used for automating business processes that require data categorization.
Quality AssuranceQuality assurance (QA) is an often overlooked but critical component to the data labeling process. Be sure to have quality checks in place if you’re managing data preparation in house. If you’re working with a data partner, they’ll have a QA process already in place. Why is QA so important? Labels on data must meet many characteristics; they must be informative, unique, and independent. The labels should also reflect a ground truth level of accuracy. For example, when labeling images for a self-driving car, all pedestrians, signs, and other vehicles must be correctly labeled within the image for the model to work successfully.
Train and TestOnce you have labeled data for training and it has passed QA, it is time to train your AI model using that data. From there, test it on a new set of unlabeled data to see if the predictions it makes are accurate. You’ll have different expectations of accuracy depending on what the needs of your model are. If your model is processing radiology images to identify infection, the accuracy level may need to be higher than a model that is being used to identify products in an online shopping experience, as one could be a matter of life and death. Set your confidence threshold accordingly.
Utilize Human-in-the-loopWhen testing your data, humans should be involved in the process to provide ground truth monitoring. Utilizing human-in-the-loop allows you to check that your model is making the right predictions, identify gaps in the training data, give feedback to the model, and retrain it as needed when low confidence or incorrect predictions are made.
ScaleCreate flexible data labeling processes that enable you to scale. Expect to iterate on these processes as your needs and use cases evolve.
Appen’s Own Data Labeling Expert: Meeta DashAt Appen, we rely on our team of experts to help provide the best possible data annotation platform. Meeta Dash, our VP of Product Management, a Forbes Tech Council Contributor, and recent winner of VentureBeat’s AI in Mentorship award, helps ensure the Appen Data Annotation Platform exceeds industry standards in providing accurate data labeling services. Her top three insights on data labeling include:
- The most successful of teams begin with a clear definition of use cases, target personas, and success metrics. This helps identify training data needs, ensure coverage across different scenarios, and mitigate potential bias due to lack of diverse datasets. Additionally incorporating a diverse pool of contributors for data labeling can help avoid any bias introduced during the labeling process.
- Data drift is more common than you may think. In the real world, the data that your model sees changes every day, and a model that you have trained a month ago may not perform as per your expectation. So it’s crucial to build a scalable, automated training data pipeline to constantly train your model with new information.
- Security and privacy considerations should be tackled head-on and not as an afterthought. Wherever possible redact sensitive data that is not needed for training an optimal model. Use a secure and enterprise-grade data labeling platform and when working on data labeling projects with sensitive data choose a secure contributor workforce that is trained to handle such data.