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Course Outline
Introduction
Overview of Kubeflow Features and Components
- Containers, manifests, and related elements.
Understanding a Machine Learning Pipeline
- Training, testing, tuning, deployment, and more.
Deploying Kubeflow to a Kubernetes Cluster
- Setting up the execution environment (e.g., training cluster, production cluster)
- Downloading, installing, and customizing the setup.
Executing a Machine Learning Pipeline on Kubernetes
- Constructing a TensorFlow pipeline.
- Building a PyTorch pipeline.
Visualizing Results
- Exporting and visualizing pipeline metrics
Customizing the Execution Environment
- Adapting the stack for diverse infrastructures
- Upgrading a Kubeflow deployment
Running Kubeflow on Public Clouds
- AWS, Microsoft Azure, Google Cloud Platform
Managing Production Workflows
- Implementing GitOps methodology
- Scheduling jobs
- Spawning Jupyter notebooks
Troubleshooting
Summary and Conclusion
Requirements
- Proficiency with Python syntax
- Experience with Tensorflow, PyTorch, or other machine learning frameworks
- A public cloud provider account (optional)
Target Audience
- Developers
- Data scientists
28 Hours
