Leveraging Active Learning for Efficient Training of Machine Learning Models with Limited Labeled Data

In today’s fast-paced and data-driven business landscape, the need for accurate and efficient machine learning (ML) models is greater than ever. Yet, one of the most significant bottlenecks in ML implementation is the data labeling process. Manual labeling is time-consuming, expensive, and prone to human error, leading to inefficiencies and misclassifications that directly affect business performance, particularly in sectors like retail, finance, and logistics.

This whitepaper introduces a comprehensive guide for data scientists, ML engineers, and business leaders on how Active Learning can streamline the ML training process by cutting down the need for extensive manual data labeling while maintaining model performance. It also highlights the financial impact of misclassification, showcases use cases of automation tools, and offers insights into how businesses can achieve up to 50% reduction in labeling efforts while improving revenue streams.