The original content is in MLOps roadmap 2024, a wonderful article. I summarize the key points in the post for my reference.
| ID | Description | Resources |
| 1 | Programming - Python & IDE - Bash & command line editors |
- IDE - visual studio code - VIM |
| 2 | Containerization and Kubernetes | - Docker - Kubernetes |
| 3 | Machine learning fundamentals | a lot |
| 4 | Machine learning fundamentals | Book: Introducing MLOps 𝖻𝗒 Mark Treveil 𝖺𝗇𝖽 Dataiku |
| 5 | MLOps components | - Git : Version control & CI/CD pipeline - Airflow: Orchestration - Mage-ai - Mlflow: Experiment tracking and model registries - Feast: feature store - KubernetesPod Operator: Model training & serving - nannyml: Monitoring & observability - Evidently: Monitoring & observability |
| 6 | Infrastructure as code | - Terraform: Infrastructure as code |
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