Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction
Overview of Kubeflow Features and Components
- Containers, manifests, and related elements.
Understanding Machine Learning Pipelines
- Processes such as training, testing, tuning, and deployment.
Deploying Kubeflow onto a Kubernetes Cluster
- Setting up the execution environment (including training and production clusters).
- Downloading, installing, and customizing the stack.
Executing Machine Learning Pipelines on Kubernetes
- Creating a TensorFlow pipeline.
- Building a PyTorch pipeline.
Visualizing Outcomes
- Exporting and displaying pipeline metrics.
Adapting the Execution Environment
- Customizing the stack for varied infrastructure needs.
- Upgrading existing Kubeflow deployments.
Running Kubeflow on Public Clouds
- Integration with AWS, Microsoft Azure, and Google Cloud Platform.
Managing Production Workflows
- Implementing GitOps methodologies.
- Scheduling automated jobs.
- Launching Jupyter notebooks.
Troubleshooting
Summary and Conclusion
Requirements
- Proficiency with 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
