Language Model Evaluation with Azure Databricks

Deploying language models is only half the battle; ensuring they produce accurate, safe, and relevant responses is critical for real-world applications. This text-based course guides you through the process of assessing and benchmarking model outputs. You will learn how to design and execute robust evaluation pipelines on Azure Databricks, transitioning from subjective manual checks to scalable, automated evaluation strategies. What you'll learn: Understand core language model evaluation metrics including accuracy, relevance, toxicity, and groundedness; Configure Azure Databricks environments to track and manage evaluation runs; Apply MLflow evaluation APIs to systematically log and compare different model versions; Implement the LLM-as-a-judge pattern to automate qualitative assessments; Analyze evaluation results to identify model biases and performance bottlenecks. The course begins with foundational concepts of model performance and key evaluation terminology. You will then progress to writing evaluation scripts, configuring MLflow, and analyzing comparative data through clear, step-by-step written tutorials. This course is designed for data scientists, developers, and AI enthusiasts who want to learn model evaluation basics; no prior experience with Databricks or advanced machine learning is required. Start building reliable AI applications by learning how to measure what matters.