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Duration 21 hours
Course Outline
Containerization Fundamentals for MLOps
- Analyzing ML lifecycle requirements
- Essential Docker concepts for ML systems
- Best practices for ensuring reproducible environments
Creating Containerized ML Training Pipelines
- Packaging model training code along with its dependencies
- Configuring training jobs via Docker images
- Handling datasets and artifacts within containers
Containerizing Validation and Model Evaluation
- Recreating evaluation environments for consistency
- Automating validation processes
- Collecting metrics and logs from containerized instances
Containerized Inference and Serving
- Architecting inference microservices
- Optimizing runtime containers for production use
- Building scalable serving architectures
Orchestrating Pipelines with Docker Compose
- Coordinating multi-container ML workflows
- Managing environment isolation and configuration
- Integrating auxiliary services such as tracking and storage
ML Model Versioning and Lifecycle Management
- Tracking models, images, and pipeline elements
- Maintaining version-controlled container environments
- Incorporating MLflow or equivalent tools
Deployment and Scaling of ML Workloads
- Executing pipelines in distributed setups
- Scaling microservices using Docker-native methods
- Monitoring containerized ML systems
Implementing CI/CD for MLOps with Docker
- Automating the build and deployment of ML components
- Testing pipelines in containerized staging environments
- Guaranteeing reproducibility and facilitating rollbacks
Conclusion and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python for data analysis or model development
- Basic familiarity with container fundamentals
Intended Audience
- MLOps engineers
- DevOps practitioners
- Data platform teams
Testimonials (2)
As i said before , for a person like me (no exp. ) this was a gateway to understanding features and functions with these programs/tools & etc. .
Patrick V. Duylovski - UBB + DZI (KBC GROUP)
Course - Docker and Kubernetes
multi-tiered, structured course programme.
