The need to automate the data labeling process
By: Expand Ai
Tags: AI, Artificial Intelligence, Supervised Learning, Machine Learning, Data Labeling

Machine Learning is the new technological wave whose impact on industries and the world can be compared to the emergence of PCs in the 1970s. While the first work now recognized as AI was done in 1943, it wasn’t until the 2010s that AI became widely used in consumer tech products and industries.

There are three types of Machine Learning Models:

1. Supervised Learning Model — where the model needs to be trained in order to perform a specific function.

2. Unsupervised Learning Model — where the model can perform a specific task without any supervision.

3. Reinforcement Learning Model — where the agent learns on its own through positive and negative reinforcements, much like a baby does.

While there has been a lot of advancement when it comes to Reinforcement Learning Models and Unsupervised Learning Models, it is Supervised Learning which is the most widely used form of Machine Learning.

With the widespread use of supervised learning models, the need for training datasets is very high in the ML industry. As a result data labeling has become a very important part of the training phase of the machine learning lifecycle.

Data Labelling or Data Annotation is an emerging sector in India, however, data labeling is primarily still done manually.

The problem?

Say you have built a supervised learning model and need a training dataset to train your supervised learning model to perform effectively. You have 5,00,000 data points that need to be labeled. Here’s what this could look like:

If you have hired in-house annotators and have five such in-house annotators and they can provide 2000 data points per person per day then it could take them ~ 7 weeks to get your data labeled

If you send your dataset to a manual data labeling firm, depending on the number of annotators working on your project the turn-around-time could still be weeks and the cost could be pretty high

So it could take you weeks and cost you hundreds of dollars by the time you get around to training your model.

Now, what if we automated the data labeling process?

Here’s what your training phase could look like then:

Instead of weeks, AI can label your data in hours which can reduce the time taken for your training phase by ~ 97% considering the example above — that it takes ~ 7 weeks for your data to be labeled

It will also be less expensive because automating the data labeling process does not incur the costs associated with manual annotations

The chance of human bias and human error entering your training dataset is low, ensuring a good quality labeled dataset which is crucial to ensure that your ML model performs optimally

Since the time taken to label your data is significantly low as compared to manual data labeling, it allows for rapid iteration to get a high-quality training dataset that ensures that your ML model performs at its best

However, even if an unsupervised learning model is used, in order to determine how well the data has been labeled we need a metric. To generate this metric, 5% — 10% of the data needs to be labeled for validation, in order to verify the predictions. Since no model is 100% accurate, thus a fully automated model is not possible.

What is the next best solution?

A combination of machine learning and human checks to ensure that the quality of the training dataset is as high as possible.

So the dataset is initially put through the ML model which predicts the initial set of labels and then in order to do away with false positives or false negatives, the labels are reviewed by humans.

This ensures that the labels are as highly accurate as possible. We save a lot of time by using Machine Learning to get initial predictions. Since we don’t have to manually label all the data points and instead just need to review the incorrect ones, this ultimately minimizes the time taken to review the predictions by ~86%. However, the amount of time that the training phase could be reduced depends on the dataset and the application.

For example, in certain tasks like image classification and text classification, AI can confidently generate high-quality labels thereby minimizing in-depth reviewing. However, for complex tasks like object detection and instance segmentation, AI can help generate initial predictions and the reviewer can rectify the wrong predictions and missing labels.

At Expand AI, we use ML-assisted labeling to generate high-quality labels at the lowest turn-around time and we also have humans in the loop to ensure high accuracy. Since we don’t incur the costs associated with manual data labeling we are also very economical with pricing.

Here’s what our pipeline looks like.

Once we get data from clients, we first clean the data and then label 5% of the data manually. We then use this data to train the model so that it can predict labels for the rest 85% of the data accurately.

We have humans in the loop to review the labels to increase the accuracy further. Finally, the labeled data is sent back to clients.

Reach out to us at test@expand-ai.com in order to test our data labeling software today!