Descriptions
LLM Optimization & Evaluation Specialization, 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. 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.
What you’ll learn
- Evaluate and optimize LLM performance using statistical testing, MLOps tools, and production monitoring systems.
- Build automated pipelines for feature engineering, experiment tracking, and data processing with industry-standard tools.
- Diagnose LLM errors, implement safety frameworks, and reduce operational costs through systematic analysis.
Who this course is for
- ML engineers, MLOps engineers, and data scientists who build and deploy large language models
- ML infrastructure and platform engineers responsible for production pipelines and cost optimization
- AI product managers and technical leads overseeing LLM features and safety
- Software engineers transitioning to ML/LLM roles and seeking production-ready skills
- Students and professionals aiming to gain practical LLM engineering experience
Specificatoin of LLM Optimization & Evaluation Specialization
- Publisher : Coursera
- Teacher : John Whitworth
- Language : English
- Level : Intermediate
- Number of Course : 13
- Duration : 4 weeks to complete at 10 hours a week
Content of LLM Optimization & Evaluation Specialization
Requirements
- Python programming, basic ML concepts, statistical fundamentals, and familiarity with Git. Experience with cloud platforms and SQL is beneficial.
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Sample Clip
Installation Guide
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Subtitle : English
Quality: 720p
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Course 1 – Engineer Features and Evaluate Models for Production
Course 2 – Optimize Deep Learning: Tune PyTorch Models
Course 3 – Evaluate & Optimize LLM Performance
Course 4 – Analyze Logs: Fix LLM Hallucinations
Course 5 – Evaluate LLMs: Test and Prove Significance
Course 6 – Optimize SQL: Build Fast Data Pipelines
Course 7 – Safeguard LLM Outputs: Test and Evaluate
Course 8 – Track and Evaluate ML Model Experiments
Course 9 – Automate Cloud Workflows with Python Scripting
Course 10 – Automate Data Pipelines: Schema Evolution
Course 11 – Develop and Evaluate LLM Features Effectively
Course 12 – Document and Evaluate LLM Prompting Success
Course 13 – Optimize LLM Costs & Streamline Processes
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3.5 GB
