Data Annotation for ML Projects – 5 Most Effective Ways to Label Data

Data annotation for machine learning projects requires the annotators to have domain knowledge, interpretation skills, and know-how of various data labeling methods. It is only then that they can devise high-quality, structured training datasets.

For instance, medical image annotation requires a data labeling workforce with basic medical knowledge, and the practical experience required to label complex X-rays, MRI, and CT scans. Lack of domain knowledge brings down the quality and context of the data that annotators label. The outputs of that model can’t be trusted in a field like healthcare.

Inaccurate punctuation, unwanted white space, and ignored case-sensitive text can lead to inaccurate training datasets. ML algorithms trained on such datasets can misjudge trends and throw biased decisions.

For example, in Speech to Text, there is no scope to annotate without punctuation. You cannot carry forward spelling mistakes. And when labeling data, it is critical to ensure that the addition of new tags complements the predefined, created an agreed-upon process.

Handling labeling projects where similar words or phrases occur but in a different order, yet with the same meaning, is a challenge. A classic example: “The horse is running towards John, and John is running towards the horse.”

This could lead to a strange order of labels, which therefore will bring in a completely different meaning to what the records show. Annotators labeling an ML dataset must operate in the same format to avoid such issues.

Halfway through the data annotation for the ML project, annotators might realize the need for a new label that is not currently present in the agreed-upon master labeling list. They may simply add it and inform other labelers too. But this will invite rework as re-examining to check if the new label fits into all relevant previous annotations.

Annotating data for ML projects is an ever-evolving process. Along with ML models, there is a need to develop constructive testing and quality validation, followed by the adoption of key learnings from their outcomes. All these require a responsive team that can accommodate changes in data volumes, increased task complexity, and task duration shifts.

These barriers become significant when you are managing and scaling your machine-learning models. We have listed here five ways that your AI and ML company can use to label and annotate data. And these come from extensive experience in handling numerous ML projects successfully.

Considering the time and dollars invested in labeling data, building the ML model, and training data; composing clear, concise, and detailed annotation guidelines is a must. It eliminates the potential of numerous mistakes across the data labeling lifecycle.

How we improve data annotation instructions

We illustrate the labels with examples. Visuals not only help our data annotators but also enable QAs to comprehend the annotation requirements better than written instructions. We show our human annotators the bigger picture and make them understand the end goals to inspire them to label accurately.

We want the ML training data to be as diverse as possible. The more diverse the training data, the more your ML algorithm is empowered with possibilities/scenarios. It results in precise and accurate decision-making and minimal or no biased decisions.

Training a model for a self-driving car solely with data collected from roads in a city will create issues when driving in mountains. Using data collected only during the day will handicap your ML model in detecting obstacles at night. For that matter, images, and videos captured from multiple angles and lighting conditions are also crucial.

We proactively identified, categorized, and labeled thousands of vehicles and pedestrian images from both live and historical traffic video feeds. Labeled images in the training dataset empowered the client’s ML model to detect queues, track stationary vehicles, and tabulate vehicle counts.

This may sound contradictory to the point mentioned above. Agree, we said that collecting and labeling diverse data is imperative. However, collecting and labeling specific data is equally necessary. Our efforts are always to feed ML models with accurate information so they can operate successfully.

For training the model for a robot waiter, using data collected solely from malls, airports, or hospitals will fail the purpose. We need to feed the model with training data collected from restaurants.

Integrating a robust multi-layered method to assess the quality of labels assures successful project results. It not only reduces rework but also saves the time and costs needed to scale up the ML project.

How we conduct quality of annotated data, images, text, or videos

• Audit process: Audit is a routine task in data labeling projects and needed to verify the work quality of human annotators. Regular quality audit helps us create datasets free of biases.
• Targeted QA: Our QA team reviews and addresses priority, all work items, labels, and annotations that are in the “disagreement” bucket.
• Random QA: We conduct regular but random sample checks of work items for every annotator.
• Observations: We use audit and QA findings to improve guidelines and train annotators.

Implementing an annotation pipeline that fits your project needs can help you maximize efficiency and minimize delivery time. We always stress on the setting up of an annotation pipeline. It has enhanced efficiency and minimized the delivery time for all our data annotations for machine learning projects.

How we develop a seamless annotation workflow

Our project transition team sets the most popular labels at the top of the list to ensure that annotators don’t waste time in trying to look for them. We also set up an annotation workflow that defines the annotation steps and automates the class and tool selection process.