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Duration 21 hours
Course Outline
Foundations of Containerisation for MLOps
- Understanding the requirements of the ML lifecycle
- Essential Docker concepts applicable to ML systems
- Best practices for ensuring reproducible environments
Constructing Containerised ML Training Pipelines
- Encapsulating model training code and dependencies
- Setting up training jobs using Docker images
- Handling datasets and artifacts within containers
Containerising Validation and Model Evaluation
- Recreating evaluation environments accurately
- Automating validation processes
- Capturing metrics and logs from containerised setups
Containerised Inference and Serving
- Architecting inference microservices
- Optimising runtime containers for production use
- Implementing scalable serving architectures
Orchestrating Pipelines 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 elements
- Utilising version-controlled container environments
- Incorporating tools like MLflow or similar platforms
Deploying and Scaling ML Workloads
- Executing pipelines in distributed environments
- Scaling microservices using Docker-native methods
- Monitoring containerised ML systems
Implementing CI/CD for MLOps with Docker
- Automating the build and deployment of ML components
- Testing pipelines in containerised staging environments
- Ensuring reproducibility and facilitating rollbacks
Summary and Next Steps
Requirements
- A solid grasp of machine learning workflows
- Proficiency in Python for data processing or model development
- Basic knowledge of container 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.
