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Duration 21 hours
Course Outline
Core Concepts of Containerization for MLOps
- Analyzing ML lifecycle requirements
- Essential Docker concepts for ML systems
- Best practices for ensuring reproducible environments
Constructing Containerized ML Training Pipelines
- Packaging model training code and its dependencies
- Setting up training jobs via Docker images
- Handling datasets and artifacts within containers
Containerizing Validation and Model Assessment
- Replicating evaluation environments consistently
- Streamlining validation workflows
- Recording metrics and logs from containerized processes
Containerized Inference and Serving
- Architecting inference microservices
- Optimizing runtime containers for production use
- Building scalable serving architectures
Orchestrating Pipelines with Docker Compose
- Managing multi-container ML workflows
- Handling environment isolation and configuration
- Connecting auxiliary services (e.g., tracking, storage)
ML Model Versioning and Lifecycle Control
- Monitoring models, images, and pipeline elements
- Maintaining version-controlled container environments
- Integrating tools like MLflow or equivalents
Deploying and Scaling ML Workloads
- Executing pipelines across distributed environments
- 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 rollback capabilities
Summary and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python for data or model development
- Basic knowledge of container fundamentals
Target Audience
- MLOps engineers
- DevOps professionals
- 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.
