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Duration 21 hours
Course Outline
Containerization Foundations for MLOps
- Analyzing ML lifecycle requirements
- Essential Docker concepts for ML systems
- Best practices for ensuring environment reproducibility
Creating Containerized ML Training Pipelines
- Encapsulating model training code and dependencies
- Setting up training jobs via Docker images
- Handling datasets and artifacts within containers
Containerizing Validation and Model Evaluation
- Recreating evaluation environments
- Streamlining validation workflows through automation
- Logging metrics and outputs from containers
Containerized Inference and Serving
- Designing inference microservices
- Tuning runtime containers for production workloads
- Implementing scalable serving architectures
Orchestrating Pipelines with Docker Compose
- Managing multi-container ML workflows
- Handling environment isolation and configuration
- Integrating auxiliary services (e.g., tracking, storage)
ML Model Versioning and Lifecycle Management
- Tracking models, images, and pipeline elements
- Maintaining version-controlled container environments
- Incorporating MLflow or comparable tools
Deploying and Scaling ML Workloads
- Executing pipelines in distributed settings
- Scaling microservices using Docker-native strategies
- Monitoring containerized ML systems
Implementing CI/CD for MLOps with Docker
- Automating the build and deployment of ML components
- Validating pipelines in containerized staging environments
- Guaranteeing reproducibility and rollback capabilities
Conclusions and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python for data or model development
- Basic familiarity with containerization fundamentals
Target 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.
