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Module 1: Introduction to MLOps

🎯 Learning Objectives​

By the end of this module, you will be able to:

  • Define MLOps and explain its importance in modern ML development
  • Compare and contrast MLOps, DevOps, and DataOps
  • Describe the complete ML lifecycle and its challenges
  • Identify MLOps maturity levels and assess organizational readiness

πŸ“š Module Content​

Chapter 1: What is MLOps?​

Understanding the definition, core principles, and why MLOps has become essential for production ML systems.

Chapter 2: MLOps vs DevOps vs DataOps​

A detailed comparison of these operational practices, including team structures and responsibilities.

Chapter 3: The ML Lifecycle​

The complete journey from data collection to model deployment, including feedback loops and retraining triggers.

⏱️ Estimated Time​

ActivityDuration
Reading2 hours
Exercises1 hour
Quiz30 minutes
Total3.5 hours

πŸ”‘ Key Concepts​

πŸ“‹ Prerequisites​

Before starting this module, ensure you have:

  • Basic understanding of machine learning concepts
  • Familiarity with software development practices
  • Knowledge of basic DevOps concepts (CI/CD, version control)

πŸš€ Let's Begin!​

Start with Chapter 1: What is MLOps? to understand the foundations.