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Duration 14 hours
Course Outline
Introduction to Kubeflow
- Exploring the mission and architectural design of Kubeflow
- An overview of core components and the broader ecosystem
- Deployment strategies and platform features
Utilizing the Kubeflow Dashboard
- Navigating the user interface effectively
- Managing notebooks and workspace configurations
- Connecting storage solutions and data sources
Foundations of Kubeflow Pipelines
- Understanding pipeline architecture and component design
- Creating pipelines using the Python SDK
- Executing, scheduling, and monitoring pipeline executions
Training ML Models via Kubeflow
- Patterns for distributed training
- Leveraging TFJob, PyTorchJob, and other operators
- Managing resources and autoscaling within Kubernetes
Serving Models with Kubeflow
- An overview of KFServing / KServe
- Deploying models using custom runtimes
- Managing revisions, scaling policies, and traffic routing
Orchestrating ML Workflows on Kubernetes
- Versioning control for data, models, and artifacts
- Integrating CI/CD processes for ML pipelines
- Implementing security protocols and role-based access control
Production ML Best Practices
- Designing resilient workflow patterns
- Establishing observability and monitoring frameworks
- Diagnosing and resolving common Kubeflow challenges
Advanced Topics (Optional)
- Configuring multi-tenant Kubeflow environments
- Scenarios for hybrid and multi-cluster deployments
- Extending Kubeflow capabilities with custom components
Summary and Future Directions
Requirements
- A foundational understanding of containerized applications
- Proficiency with basic command-line operations
- Familiarity with core Kubernetes concepts
Target Audience
- Machine learning practitioners
- Data scientists
- DevOps teams looking to adopt Kubeflow
Testimonials (4)
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The knowledge and exchanges with Augustin