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Duration 21 hours
Course Outline
Foundations of Containerization for MLOps
- Comprehending ML lifecycle requirements
- Essential Docker concepts for ML systems
- Best practices for establishing reproducible environments
Constructing Containerized ML Training Pipelines
- Packaging model training code alongside its dependencies
- Configuring training jobs via Docker images
- Handling datasets and artifacts within containers
Containerizing Validation and Model Evaluation
- Replicating evaluation environments
- Automating validation workflows
- Recording metrics and logs from containers
Containerized Inference and Serving
- Architecting inference microservices
- Optimizing runtime containers for production use
- Building scalable serving architectures
Pipeline Orchestration using Docker Compose
- Managing multi-container ML workflows
- Isolating environments and handling configuration management
- Integrating auxiliary 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
Deploying and Scaling ML Workloads
- Executing pipelines in distributed settings
- Scaling microservices with Docker-native methods
- Monitoring containerized ML systems
CI/CD for MLOps with Docker
- Automating the build and deployment of ML components
- Testing pipelines within containerized staging environments
- Guaranteeing reproducibility and rollback capabilities
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python for data or model development
- Basic familiarity with 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.
