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.
Applied Learning Project
Apply your skills through industry-relevant projects including building feature engineering pipelines with MLOps tools, creating statistical testing frameworks to evaluate LLM performance, diagnosing and resolving hallucination issues through data analysis, optimizing vector search and SQL queries for production systems, and developing comprehensive safety testing suites. You'll also track ML experiments using version control systems, automate cloud workflows with Python scripts, build data pipelines with Apache Airflow, and create complete product requirements and testing documentation for LLM features.
