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Duration 21 hours
Course Outline
Containerization Fundamentals for MLOps
- Analyzing ML lifecycle requirements
- Essential Docker concepts for ML systems
- Best practices for ensuring reproducibility
Creating Containerized ML Training Pipelines
- Bundling model training code and dependencies
- Setting up training jobs via Docker images
- Handling datasets and artifacts within containers
Containerizing Validation and Model Assessment
- Recreating consistent evaluation environments
- Streamlining validation processes
- Collecting metrics and logs from containers
Containerized Inference and Serving
- Building inference microservices
- Optimizing runtime containers for production use
- Implementing scalable serving models
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 Oversight
- Monitoring models, images, and pipeline elements
- Maintaining version-controlled container setups
- Integrating tools like MLflow or similar platforms
Deploying and Scaling ML Workloads
- Executing pipelines in distributed systems
- Scaling microservices using Docker-native methods
- Overseeing containerized ML systems
CI/CD for MLOps using Docker
- Automating the build and deployment of ML parts
- Testing pipelines in containerized staging areas
- Guaranteeing reproducibility and rollback capabilities
Summary and Future Directions
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python for data analysis or model development
- Basic knowledge of container concepts
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.
