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Course Outline
Introduction
Exploring Kubeflow Features and Components
- Understanding containers, manifests, and related elements.
Understanding the Machine Learning Pipeline
- Processes such as training, testing, tuning, and deployment.
Deploying Kubeflow on a Kubernetes Cluster
- Setting up the execution environment, including training and production clusters.
- Processes for downloading, installing, and customizing the stack.
Executing a Machine Learning Pipeline on Kubernetes
- Constructing a TensorFlow pipeline.
- Building a PyTorch pipeline.
Visualizing Results
- Exporting and visualizing key pipeline metrics.
Customizing the Execution Environment
- Adapting the stack for varied infrastructure needs.
- Methods for upgrading a Kubeflow deployment.
Operating Kubeflow on Public Clouds
- Deployment on AWS, Microsoft Azure, and Google Cloud Platform.
Overseeing Production Workflows
- Implementing GitOps methodologies.
- Scheduling automated jobs.
- Creating and managing Jupyter notebooks.
Troubleshooting Strategies
Summary and Conclusion
Requirements
- A basic understanding of Python syntax.
- Practical experience with Tensorflow, PyTorch, or other machine learning frameworks.
- An account with a public cloud provider (optional).
Target Audience
- Developers
- Data scientists
28 Hours
