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Duration 14 hours
Course Outline
Introduction to Kubeflow
- Exploring the mission and architecture of Kubeflow
- Overview of core components and the broader ecosystem
- Deployment strategies and platform capabilities
Interacting with the Kubeflow Dashboard
- Navigating the user interface
- Managing notebooks and workspaces
- Integrating storage solutions and data sources
Foundations of Kubeflow Pipelines
- Pipeline structure and component design principles
- Developing pipelines using the Python SDK
- Execution, scheduling, and monitoring of pipeline runs
Training ML Models on Kubeflow
- Distributed training methodologies
- Utilizing TFJob, PyTorchJob, and other operators
- Resource management and autoscaling within Kubernetes
Serving Models with Kubeflow
- Introduction to KFServing and KServe
- Deploying models using custom runtimes
- Handling revisions, scaling, and traffic routing
Overseeing ML Workflows on Kubernetes
- Versioning of data, models, and artifacts
- Integrating CI/CD processes for ML pipelines
- Security considerations and role-based access control
Best Practices for Production ML
- Designing dependable workflow patterns
- Implementing observability and monitoring
- Resolving common Kubeflow issues
Advanced Topics (Optional)
- Setting up multi-tenant Kubeflow environments
- Hybrid and multi-cluster deployment strategies
- Extending Kubeflow through custom components
Conclusion and Future Steps
Requirements
- A solid grasp of containerized applications
- Proficiency with fundamental command-line operations
- Basic knowledge of Kubernetes concepts
Target Audience
- Machine Learning practitioners
- Data scientists
- DevOps teams exploring Kubeflow for the first time
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
basic understanding of container/kubernetes and how they interact features of the openshift plattform