Table of Contents

What Is MLOps? Architecture, MLOps vs DevOps, and LLMOps

What Is MLOps?

Why MLOps Matters for Production Machine Learning

Core Principles of MLOps 1. Automation

2. Reproducibility

3. Scalability

4. Monitoring and Governance

MLOps vs DevOps: Key Lifecycle Differences

MLOps Architecture: How It All Fits Together Data Layer

Experimentation and Training Layer

Deployment Layer

Monitoring and Feedback Layer

Governance and Compliance Layer

MLOps Maturity Levels Level 0: Manual and Ad Hoc

Level 1: Partial Automation

Level 2: Full Automation

MLOps Tools for Data, Training, Deployment, and Monitoring Tools for Data Versioning and Experiment Tracking

Model Training Frameworks and Feature Stores

Model Serving, Deployment, and Monitoring Tools

End-to-End Machine Learning Platforms

What Is LLMOps?…