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Course Outline
Introduction
Overview of Kubeflow Features and Components
- Containers, manifests, and related concepts.
Understanding a Machine Learning Pipeline
- Stages such as training, testing, tuning, and deployment.
Deploying Kubeflow to a Kubernetes Cluster
- Preparing the execution environment (e.g., training cluster, production cluster).
- Downloading, installing, and customizing the stack.
Running a Machine Learning Pipeline on Kubernetes
- Constructing a TensorFlow pipeline.
- Building a PyTorch pipeline.
Visualizing the Results
- Exporting and analyzing pipeline metrics.
Customizing the Execution Environment
- Adapting the stack for varied infrastructures.
- Upgrading an existing Kubeflow deployment.
Operating Kubeflow on Public Clouds
- Integration with AWS, Microsoft Azure, and Google Cloud Platform.
Managing Production Workflows
- Implementing GitOps methodology.
- Scheduling automated jobs.
- Spawning and managing Jupyter notebooks.
Troubleshooting Strategies
Summary and Conclusion
Requirements
- Proficiency with Python syntax
- Hands-on experience with TensorFlow, PyTorch, or other machine learning frameworks
- Access to a public cloud provider account (optional)
Target Audience
- Developers
- Data Scientists
28 Hours
