Introduction to Machine Learning Models (AI) Testing

This course will introduce you to the World of Machine Learning Models Testing. As AI continues to revolutionize industries, many companies are developing their own ML models to enhance their business operations. However, testing these models presents unique challenges that differ from traditional software testing. Machine Learning Model testing requires a deeper understanding of both data quality and model behavior, as well as the algorithms that power them. This Course starts with explaining the fundamentals of the Artificial Intelligence & Machine Learning concepts and gets deep dive into testing concepts & Strategies for Machine Learning models with real time examples. Please Note:This course highlights specialized testing types and methodologies unique to Machine Learning Testing, with real-world examples. No specific programming language or code is involved in this tutorial.

RAG-LLM Evaluation & Test Automation for Beginners

LLMs are everywhere! Every business is building its own custom AI-based RAG-LLMs to improve customer service. But how are engineers testing them? Unlike traditional software testing, AI-based systems need a special methodology for evaluation. This course starts from the ground up, explaining the architecture of how AI systems (LLMs) work behind the scenes. Then, it dives deep into LLM evaluation metrics. This course shows you how to effectively use the RAGAS framework library to evaluate LLM metrics through scripted examples. This allows you to use Pytest assertions to check metric benchmark scores and design a robust LLM Test/evaluation automation framework. What will you learn from the course? High level overview on Large Language Models (LLM) Understand how Custom LLM’s are built using Retrieval Augmented Generation (RAG) Architecture Common Benchmarks/Metrics used in Evaluating RAG based LLM’s Introduction to RAGAS Evaluation framework for evaluating/test LLM’s