MLOps & Deployment Pipeline
Building robust deployment pipelines for machine learning models
About This Project
This project focuses on designing and implementing a continuous integration/deployment pipeline to train, register, and deploy machine learning models on cloud platforms like AWS, Azure, or GCP. It includes building monitoring and alerting systems for model performance degradations, container orchestration using technologies like Kubernetes, and demonstrating consistent model registry practices with MLflow, CI/CD best practices, and basic A/B test infrastructure.
Core Concepts
- MLOps principles and practices
- CI/CD for machine learning
- Container orchestration with Kubernetes
- Model registry and versioning
- Monitoring and alerting systems
- A/B testing infrastructure
Key Knowledge/Skills
- MLOps principles
- Cloud deployment strategies
- Infrastructure as code
- Containerization technologies
- Continuous integration and delivery
- Model monitoring and maintenance
Coursework Covered
Applied Systems and MLOps