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

data annotation best practices to power ml algorithms
The quality of the training dataset you use for your ML model determines the quality of output. High-quality training data will lead ML models to make precise decisions that empower you to grow profitably.

As the head of Business Process Management at Hitech BPO, it is my job to ensure comprehensive and accurate training datasets for Machine Learning (ML) models for our clients. And the problems and solutions in data annotation discussed here are those we find crucial for every ML project.

Over the past few years, the conversation about machine learning has revolved around how this new technology can automate tasks and improve operational efficiencies. That said, these conversations avoid discussing the unexciting part of successful machine learning projects. The sweat and hours that go into preparing high-quality training datasets are a part that is often overlooked.

Any automation setup needs extreme levels of care and attention right from the ground level. Or, instead of automating tasks, you end up automating errors. Therefore, adopting ML technology, without due attention to data labeling quality, leads to disasters.

For instance, Amazon had to scrap its secret recruiting tool, because it carried bias against women. It happened because Amazon’s ML models were trained to vet applicants based on the data of former applicant resumes. No one labeling the data noticed the information was about male applicants. And relying on that skewed data created a biased ML model. This shows how the ML projects of leading AI and ML companies are ruined due to a lack of alertness regarding data annotation.

So, I think it’s time to bring up quality issues related to data labeling for ML projects before discussing the operational activities that can be fast-tracked using ML models. This is not only about how to label data but also about a clear understanding of data annotation pitfalls that crash your ML projects.

Top 5 concerns in any data labeling project

As a leading data annotation company, we provide accurately labeled datasets. These datasets are for top AI and ML solution providers to build machine learning, AI, and deep learning models. We assist them with the latest tools, technology, and expert human annotators who can train, validate, and tune their ML models. And according to me, here are the primary challenges we find in data labeling for ML projects:

primary challenges of data labeling projects

1. Lack of domain knowledge

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.

2. Punctuation issues, excess white space, and case sensitivity rules

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.

3. Nested and inverted data tags

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.

4. Adding new labels to the project mid-term

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.

5. Agile yet Inflexible

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.

5 Most Effective Data Annotation Methods for Your Machine Learning Projects

At Hitech BPO, we call it “data annotation for ML lifecycle.” The approach is to place the focus on data at every step of the ML lifecycle. And adhering to these best practices reduces the complexity of labeling data and text, annotating videos and images, etc. for machine learning models.

effective data labeling methods

1. Set up annotation guidelines

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.

2. Collect and label diverse data

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.

3. Collect targeted/focused data

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.

4. Establish a QA process

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.

5. Finding the most suitable annotation pipeline

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.


Before summing up, I would like to address another issue just to make sure that I don’t pass by a vital topic which deserves due attention.

Running a pilot project – The complexity of a data annotation for machine learning project indicates if a pilot project should be conducted or not. Keep in mind that paid pilot projects see more successful results. Always try to run a free pilot project with your data annotation outsourcing partner to get accurately labeled data.

Our take on pilot projects

At Hitech BPO, we always test the water in collaboration with our clients. From a pilot project, we:

  • Calculate project completion time
  • Assess the performance of data labelers
  • Domain knowledge of QAs
  • Improve our guidelines and processes

Upon completion of the pilot project, we use the findings to complete mutually agreed targets for the workforce, budgets, and data security.

To wrap it up, I would like to stress once again that focusing on the “data for the ML lifecycle” is about improving the success rate of your ML projects. Better training data for ML models ensures higher output quality and returns on investments. A focus on high-quality training data can help you find success with your ML projects while ignoring the importance of data labeling can cause disasters.

Author Snehal Joshi
About Author:

 spearheads the business process management vertical at Hitech BPO, an integrated data and digital solutions company. Over the last 20 years, he has successfully built and managed a diverse portfolio spanning more than 40 solutions across data processing management, research and analysis and image intelligence. Snehal drives innovation and digitalization across functions, empowering organizations to unlock and unleash the hidden potential of their data.

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