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Course Outline
Introduction
Overview of Kubeflow Features and Components
- Containers, manifests, and related elements.
Introduction to Machine Learning Pipelines
- Training, testing, tuning, deployment, and other phases.
Deploying Kubeflow to a Kubernetes Cluster
- Setting up the execution environment (e.g., training cluster, production cluster)
- Downloading, installing, and applying customizations.
Executing Machine Learning Pipelines on Kubernetes
- Developing a TensorFlow pipeline.
- Developing a PyTorch pipeline.
Visualizing Outcomes
- Exporting and displaying pipeline metrics
Tailoring the Execution Environment
- Adapting the stack for various infrastructures
- Upgrading Kubeflow deployments
Operating Kubeflow on Public Clouds
- AWS, Microsoft Azure, Google Cloud Platform
Oversight of Production Workflows
- Implementing GitOps methodology
- Scheduling tasks
- Generating Jupyter notebooks
Diagnostic and Troubleshooting
Wrap-up and Conclusions
Requirements
- Proficiency in Python syntax
- Hands-on experience with Tensorflow, PyTorch, or alternative machine learning frameworks
- Access to a public cloud provider account (optional)
Target Audience
- Software Developers
- Data Scientists
28 Hours
