With data becoming cheaper to collect and store, data scientists are often left overwhelmed by the sheer volume of unlabeled data. Active learning (machine learning) helps them make sense of it all.

Algorithms are typically used to actively select the data the machine is learning from and training on. The machine learns and can choose from a pool of unclassified data to label as similar to the training data. It can then continually train on this incoming data.

The overall goal of active learning as part of machine learning is to minimize how much labeled data the machine needs to train on, while maximizing its overall performance moving forward. That's why data scientists use active learning tools to enhance machine learning, annotating, and labeling data used in the training stage.

Active learning generally operates through an interactive loop-based process. Here's what the process of active learning in machine learning looks like.

Source: Thoughtworks

We've learned that active learning enhances model training by selecting the most valuable data points from an unlabeled dataset. This process of selecting data points, or query strategy, can be categorized into the following three methods.

It's active learning when data arrives continuously, like in real-time analysis. The model processes data one piece at a time and selects the most useful samples for labeling to improve its accuracy. Two common strategies for selection are:

This approach is great for live scenarios, like analyzing video streams, where waiting for a batch of data isn't possible. It saves labeling costs, adapts to changing data, and scales well. However, it can face challenges like bias, selecting less helpful samples, and relying on the streaming setup.

With this method, the model selects the most valuable data points from a pool of unlabeled data for labeling, focusing only on examples that can improve its accuracy. Pool-based sampling saves time, cost, and resources and accelerates learning by targeting the most informative samples. However, its effectiveness depends on the quality of the unlabeled data pool and the sampling strategy. Poorly selected data or ineffective methods can lower model performance, and it may not work well with unstructured or noisy data. Also, due to the size of datasets, it often requires substantial digital memory.

Query synthesis methods are techniques used in active learning to generate new samples for labeling from existing data. This approach is useful when labeled data is limited or expensive to obtain. By creating diverse training data, these methods help improve the model's performance. Here's what to do:

These synthetic samples are labeled by an annotator and added to the training dataset, providing the model with more representative and diverse training data.

Some limitations of this approach include:

When training machine learning models, the approach to data labeling and selection plays a crucial role in determining efficiency and performance. Active learning and passive learning are two distinct strategies used for this purpose. The table below highlights the key differences between these approaches:

Both active learning and reinforcement learning are focused on reducing the amount of labels needed to develop a model but operate from different perspectives.

As discussed before, this technique selects the most valuable samples from an unlabeled dataset and queries a human annotator for their labels. It enhances the model's accuracy while keeping labeling costs low. Active learning is particularly beneficial in areas like medical imaging and natural language processing (NLP), where labeling can be expensive and time-consuming.

Reinforcement learning, on the other hand, focuses on training an agent to make a series of decisions within an environment. The agent learns by interacting with the environment and receiving feedback through rewards or penalties based on its actions. This method is commonly applied in robotics and autonomous systems. Reinforcement learning aims to maximize cumulative rewards over time, encouraging the agent to explore and optimize its actions to achieve long-term objectives.

There are several key benefits to active learning within machine learning, largely focused on speed and costs for data scientists.

Large datasets take up significant memory and are expensive to parse and label. By reducing the amount of data being labeled, active learning can significantly minimize budget outgoings. Auto-segmentation rules can also help keep costs down while ensuring that the data being used is the most significant for the expected outcome.

Convergence is a vital part of machine learning. During training, the model settles losses to a point where additional training won't improve the model any further. Active learning helps reach this point of convergence faster by focusing only on the most relevant data samples.

Using the most informative samples for labeling, accuracy can be achieved faster and improve the model's performance. Active learning models are designed to choose the data samples that reduce the model's uncertainty, while aiming for greater accuracy over time.

Active learning finds applications across various domains. Here are a few examples:

Using active learning techniques to train your AI models is one of the best ways to save money on large machine learning projects while speeding up iteration times before reaching crucial convergence levels. Explore new technology and develop your models into usable, useful projects with these techniques!