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Duration 21 hours
Course Outline
Foundations of Containerisation for MLOps
- Understanding ML lifecycle requirements
- Key Docker concepts for ML systems
- Best practices for reproducible environments
Constructing Containerised ML Training Pipelines
- Packaging model training code and dependencies
- Configuring training jobs via Docker images
- Managing datasets and artifacts within containers
Containerising Validation and Model Evaluation
- Reproducing evaluation environments
- Automating validation workflows
- Capturing metrics and logs from containers
Containerised Inference and Serving
- Designing inference microservices
- Optimising runtime containers for production
- Implementing scalable serving architectures
Pipeline Orchestration with Docker Compose
- Coordinating multi-container ML workflows
- Environment isolation and configuration management
- Integrating supporting services (e.g., tracking, storage)
ML Model Versioning and Lifecycle Management
- Tracking models, images, and pipeline components
- Version-controlled container environments
- Integrating MLflow or similar tools
Deploying and Scaling ML Workloads
- Running pipelines in distributed environments
- Scaling microservices using Docker-native approaches
- Monitoring containerised ML systems
CI/CD for MLOps with Docker
- Automating builds and deployment of ML components
- Testing pipelines in containerised staging environments
- Ensuring reproducibility and rollbacks
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python for data or model development
- Basic knowledge of container fundamentals
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.
