Data labeling is a crucial part of supervised machine learning modeling. Your in-house data or data acquired from external sources must be cleaned, labeled, and annotated to effectively train, test, and validate your machine learning models. But who labels your data? Do you have enough skilled employees to label your data in-house? Are they trained on the processes and tools to label your data? Do you have a list of data labeling best practices for your labelers to follow? We will answer these questions in this post.
Phases of data labelingData labeling contains four phases: 1 - Data collection: you may acquire data from external sources, look to use your in-house data, or a combination of both. The first phase of data labeling starts with collecting and collating data in one place. 2 - Data tagging: When you collect data, most of it will be unlabeled. This is where your labelers spend time sifting through each column of data and tag each data element. 3 - Checking data labeling quality: As your labelers begin tagging and labeling data, you need a process to quality-check the labeling for accuracy. Besides labelers, you need QA inspectors (managers or admins) to review labeled data against a predefined checklist to meet quality data labeling requirements. 4 - Training your ML models: As the data get labeled and quality-checked, you may hand over the data to your ML engineers to train the ML models. The ML model's output will determine the accuracy of labeled data.
Best practices of data labelingYour in-house data labeling efforts may involve many people—labelers, managers, admins, QA specialists, etc. To make everyone's job easy, you need a well-defined set of guidelines and best practices to label your data quickly, accurately, and cost-effectively. Errors or delays in labeling your data adds to your ML budget. Here is a checklist of seven simple points you need to address to make your data labeling effective and friction-free. 1 - Collect diverse, specific data: Diverse data minimizes bias, and collecting specific data makes your ML models more accurate. What is specific data? Let's say you want to build an AI solution to create a robot-waiter. Collecting data from restaurants is specific data. Collecting data from airport food courts and mall eateries isn't. 2 - Set up a data labeling guideline: Create a guideline that defines the labeling process, labeling names and tags, and how to use the tools. 3 - Create a visual, easily understandable data labeling workflow: A workflow visually defines the labeling process. This is easy to remember and refer to when needed. 4 - Establish communication: Establish a clear line of communication between labelers, admins, QA, and ML engineers. 5 - Establish a QA process: Integrate a QA method into your project pipeline to assess the quality of the labels and guarantee successful project results.
Three ways to conduct quality checks6 - Provide regular feedback to labelers: Communicate annotation errors with your workforce for a more streamlined QA process. 7 - Run a data labeling pilot project: Put your workforce, annotation guidelines, and project processes to test by running a pilot project.
- #1 Timely audits: Your QA folk need to perform quality checks at regular intervals.
- #2 Targeted discussions: Allow your QA and labelers to discuss disagreements in labeling patterns, conventions, and processes.
- #3 Random checks: This should happen besides regular quality checks to test the quality of data labeling.