Start Here
Updated May 15, 2023 ·
This section contains notes and references covering the full Machine Learning and MLOps, from core ML concepts, to building models, deploying them, and eventually automating entire ML platforms.
| Section | Purpose |
|---|---|
| Projects | Practical implementations and end-to-end ML projects. |
| ML Fundamentals | Core theory and concepts behind Machine Learning and AI. |
| ML Lifecycle | End-to-end ML workflow from data collection to model evaluation. |
| MLOps Concepts | Operational and organizational concepts to run ML in production. |
| MLOps Deployment | Focuses on deploying, serving, testing, and monitoring ML models. |
| Developing Models | Building reliable, reproducible, and maintainable ML systems. |
| Fully Automated MLOps | Automation of ML pipelines, orchestration, and continuous operations. |
| MLOps on Kubernetes | Scalable ML infrastructure using Kubernetes and other tools for production. |