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.
Overview of a Machine Learning Pipeline
- Training, testing, tuning, deployment, and more.
Deploying Kubeflow to a Kubernetes Cluster
- Preparing the execution environment (training cluster, production cluster, etc.)
- Downloading, installation, and customization processes.
Running a Machine Learning Pipeline on Kubernetes
- Constructing a TensorFlow pipeline.
- Building a PyTorch pipeline.
Visualizing the Results
- Exporting and visualizing pipeline metrics
Customizing the Execution Environment
- Adapting the stack for diverse infrastructure setups
- Upgrading Kubeflow deployments
Running Kubeflow on Public Clouds
- AWS, Microsoft Azure, and Google Cloud Platform
Managing Production Workflows
- Implementing GitOps methodologies
- Scheduling jobs
- Spawning Jupyter notebooks
Troubleshooting
Summary and Conclusion
Requirements
- Proficiency with Python syntax
- Hands-on experience with Tensorflow, PyTorch, or other machine learning frameworks
- An account with a public cloud provider (optional)
Target Audience
- Software Developers
- Data Scientists
28 Hours
