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Duration 21 hours
Course Outline
Foundations of Containerization for MLOps
- Understanding ML lifecycle requirements
- Key Docker concepts applicable to ML systems
- Best practices for creating reproducible environments
Building Containerized ML Training Pipelines
- Packaging model training code and dependencies
- Configuring training jobs via Docker images
- Managing datasets and artifacts within containers
Containerizing Validation and Model Evaluation
- Reproducing consistent evaluation environments
- Automating validation workflows
- Capturing metrics and logs from containers
Containerized Inference and Serving
- Designing inference microservices
- Optimizing runtime containers for production use
- Implementing scalable serving architectures
Pipeline Orchestration with Docker Compose
- Coordinating multi-container ML workflows
- Managing environment isolation and configuration
- Integrating supporting services such as tracking and storage
ML Model Versioning and Lifecycle Management
- Tracking models, images, and pipeline components
- Maintaining version-controlled container environments
- Integrating tools like MLflow for enhanced management
Deploying and Scaling ML Workloads
- Running pipelines in distributed environments
- Scaling microservices using Docker-native methods
- Monitoring the health and performance of containerized ML systems
CI/CD for MLOps with Docker
- Automating the build and deployment of ML components
- Testing pipelines in containerized staging environments
- Ensuring reproducibility and facilitating rollbacks
Summary and Next Steps
Requirements
- A solid understanding of machine learning workflows
- Experience developing data or models using Python
- Familiarity with the core concepts of containerization
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.
