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Course Outline
Introduction to Kubeflow
- Grasping the Kubeflow mission and architecture
- Overview of core components and the ecosystem
- Deployment options and platform capabilities
Working with the Kubeflow Dashboard
- Navigating the user interface
- Managing notebooks and workspaces
- Integrating storage and data sources
Kubeflow Pipelines Fundamentals
- Pipeline structure and component design
- Authoring pipelines using the Python SDK
- Executing, scheduling, and monitoring pipeline runs
Training ML Models on Kubeflow
- Distributed training patterns
- Utilizing TFJob, PyTorchJob, and other operators
- Resource management and autoscaling within Kubernetes
Model Serving with Kubeflow
- Overview of KFServing / KServe
- Deploying models with custom runtimes
- Managing revisions, scaling, and traffic routing
Managing ML Workflows on Kubernetes
- Versioning data, models, and artifacts
- Integrating CI/CD for ML pipelines
- Security and role-based access control
Best Practices for Production ML
- Designing reliable workflow patterns
- Observability and monitoring
- Troubleshooting common Kubeflow issues
Advanced Topics (Optional)
- Multi-tenant Kubeflow environments
- Hybrid and multi-cluster deployment scenarios
- Extending Kubeflow with custom components
Summary and Next Steps
Requirements
- A foundational understanding of containerized applications
- Experience with fundamental command-line operations
- Familiarity with Kubernetes concepts
Target Audience
- ML practitioners
- Data scientists
- DevOps teams new to Kubeflow
14 Hours
Testimonials (2)
About the microservices and how to maintenance kubernetes
Yufri Isnaini Rochmat Maulana - Bank Indonesia
Course - Advanced Platform Engineering: Scaling with Microservices and Kubernetes
The training met expectations with its clear explanations, real-world examples, and hands-on labs that made complex topics easy to understand. It provided valuable insights into container orchestration, security, scaling and many other advanced topics.
