LLM Optimization & Evaluation

About This Course

Learn the complete lifecycle of LLM optimization and evaluation through hands-on experience with production-ready techniques. This comprehensive specialization equips you with essential skills to evaluate, optimize, and deploy large language models effectively. You'll learn to engineer features for ML models, implement rigorous statistical testing for LLM performance, diagnose and fix hallucinations through log analysis, optimize both computational costs and database performance, and build robust safety testing frameworks. The program progresses from foundational ML concepts through advanced MLOps practices, covering experiment tracking with tools like DVC and W&B, automated cloud workflows, data pipeline management with Apache Airflow, and product development workflows including requirements documentation and user acceptance testing. Through practical projects, you'll analyze LLM spend reports to reduce operational costs, implement value-stream mapping to streamline ML pipelines, create comprehensive testing suites with mutation testing, and develop operational runbooks for production systems. Whether you're optimizing SQL queries for vector search, conducting A/B tests for model improvements, or building automated monitoring systems, this specialization provides the technical depth and practical experience needed to excel in LLM engineering roles.

Instructor

Coursera