Course Outline
Introduction
Overview of Kubeflow Features and Components
- Containers, manifests, etc.
Overview of a Machine Learning Pipeline
- Training, testing, tuning, deploying, etc.
Deploying Kubeflow to a Kubernetes Cluster
- Preparing the execution environment (training cluster, production cluster, etc.)
- Downloading, installing, and customising.
Running a Machine Learning Pipeline on Kubernetes
- Building a TensorFlow pipeline.
- Building a PyTorch pipeline.
Visualising the Results
- Exporting and visualising pipeline metrics
Customising the Execution Environment
- Customising the stack for diverse infrastructures
- Upgrading a Kubeflow deployment
Running Kubeflow on Public Clouds
- AWS, Microsoft Azure, Google Cloud Platform
Managing Production Workflows
- Operating with the GitOps methodology
- Scheduling jobs
- Launching Jupyter notebooks
Troubleshooting
Summary and Conclusion
Requirements
- Familiarity with Python syntax
- Experience with TensorFlow, PyTorch, or other machine learning frameworks
- An account with a public cloud provider (optional)
Audience
- Developers
- Data scientists
Testimonials (1)
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
