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100DaysOfMLOps
A continuous learning journey to master Machine Learning Operations (MLOps), covering model versioning, training pipelines, deployment, and monitoring.
60 Days
Current Streak
60%
Completion
60/100
Total Days
The #100DaysOfMLOps challenge is a dedicated commitment to bridging the gap between machine learning model development and operational production. Over 100 days, I am learning and implementing tools to automate, monitor, and scale ML pipelines.
🎯 Learning Objectives
- Data & Model Versioning: Tracking dataset versions and model artifacts using tools like DVC and MLflow.
- Pipeline Automation: Building automated pipelines for data prep, training, and validation.
- Model Deployment: Packaging models as microservices (FastAPI/Docker) and deploying to Kubernetes.
- Monitoring & Observability: Tracking data drift, model performance decay, and latency in production.
🛠️ Core Topics Explored
- Versioning: DVC (Data Version Control), Git LFS.
- Tracking & Registry: MLflow, Weights & Biases.
- Containerization & Orchestration: Docker, Kubernetes, Kubeflow, Prefect.
- Model Serving: FastAPI, Seldon Core, Triton.
- CI/CD for ML: GitHub Actions for automated model testing and build.