MLOps and LLMOps Complete

Welcome to MLOps and LLMOps Complete, a comprehensive 14-week course designed for MLOps Engineers, DevOps Engineers, Platform Engineers, and ML Engineers who want to master production-grade machine learning systems.

🎯 Course Overview

This course covers the complete lifecycle of deploying and operating machine learning systems at scale, from traditional MLOps practices to cutting-edge LLMOps techniques.

What You'll Learn

Part	Focus Area	Modules
Part 1	MLOps Foundations	Modules 1-9
Part 2	LLMOps Specialization	Modules 10-14

📚 Course Modules

Part 1: MLOps (Modules 1-9)

1. Introduction to MLOps - Fundamentals, maturity levels, and the ML lifecycle
2. Version Control for ML - Git, DVC, and model versioning strategies
3. Experiment Tracking - MLflow, Weights & Biases, and experiment management
4. Data Pipelines - ETL, feature stores, and data quality
5. Training Infrastructure - GPU management, distributed training, cloud platforms
6. Model Serving - REST APIs, serving frameworks, containerization
7. Kubernetes for ML - K8s basics, Kubeflow, scaling ML workloads
8. CI/CD for ML - Automation, testing, continuous training
9. Monitoring & Observability - Performance monitoring, drift detection, alerting

Part 2: LLMOps (Modules 10-14)

10. LLMOps Fundamentals - What makes LLMOps different
11. LLM Deployment - Serving at scale, inference optimization
12. LLM Evaluation - Automated testing, human evaluation, safety
13. Prompt Management - Versioning, A/B testing, optimization
14. Enterprise LLMOps - Security, compliance, platform building

🛠️ Technologies Covered

📋 Prerequisites

Before starting this course, you should have:

• Python proficiency - Comfortable with Python programming
• ML fundamentals - Understanding of machine learning concepts
• Linux/CLI experience - Familiarity with command-line tools
• Basic DevOps knowledge - Understanding of CI/CD concepts
• Cloud experience - Exposure to at least one cloud provider (AWS, GCP, Azure)

🎓 Course Structure

Each module includes:

• 📖 Comprehensive chapters with detailed explanations
• 💻 Code examples and infrastructure templates
• 🔧 Hands-on exercises with real-world scenarios
• 📝 30 quiz questions to test your understanding

📊 Assessment

Assessment	Questions	Weight
Module Quizzes (14)	30 each	70%
Final Exam	50	30%
Total	470	100%

🚀 Getting Started

Ready to begin your MLOps journey?

1. Start with Module 1: Introduction to MLOps
2. Complete each chapter in order
3. Take the module quiz before moving forward
4. Use the Glossary for terminology reference
5. Explore Resources for additional learning

💡 Tips for Success

Study Strategy

1. Practice hands-on - Don't just read, implement
2. Build projects - Apply concepts to real scenarios
3. Review regularly - Revisit previous modules
4. Join the community - Discuss with peers

Course Code

MLOPS-401 | Winter 2025 | 14 Weeks

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Let's build production-grade ML systems! 🚀