Course Outline
Introduction to Kubeflow for Government
- Understanding the Mission and Architecture of Kubeflow
- Overview of Core Components and Ecosystem
- Deployment Options and Platform Capabilities
Working with the Kubeflow Dashboard for Government
- Navigation of User Interface
- Managing Notebooks and Workspaces
- Integrating Storage and Data Sources
Kubeflow Pipelines Fundamentals for Government
- Structure and Component Design of Pipelines
- Authoring Pipelines with Python SDK
- Executing, Scheduling, and Monitoring Pipeline Runs
Training Machine Learning Models on Kubeflow for Government
- Patterns for Distributed Training
- Using TFJob, PyTorchJob, and Other Operators
- Resource Management and Autoscaling in Kubernetes
Model Serving with Kubeflow for Government
- Overview of KFServing / KServe
- Deploying Models with Custom Runtimes
- Managing Revisions, Scaling, and Traffic Routing
Managing Machine Learning Workflows on Kubernetes for Government
- Versioning Data, Models, and Artifacts
- Integrating CI/CD for ML Pipelines
- Security and Role-Based Access Control
Best Practices for Production Machine Learning for Government
- Designing Reliable Workflow Patterns
- Observability and Monitoring
- Troubleshooting Common Kubeflow Issues
Advanced Topics (Optional) for Government
- Multi-Tenant Kubeflow Environments
- Hybrid and Multi-Cluster Deployment Scenarios
- Extending Kubeflow with Custom Components
Summary and Next Steps for Government
Requirements
- An understanding of containerized applications for government use.
- Experience with basic command-line workflows.
- Familiarity with Kubernetes concepts.
Audience
- Machine learning practitioners
- Data scientists
- DevOps teams new to Kubeflow for government applications
Testimonials (5)
he was patience and understood that we fall behind
Albertina - REGNOLOGY ROMANIA S.R.L.
Course - Deploying Kubernetes Applications with Helm
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.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life TM
Course - Kubeflow
It gave a good grounding for Docker and Kubernetes.
Stephen Dowdeswell - Global Knowledge Networks UK
Course - Docker (introducing Kubernetes)
I mostly enjoyed the knowledge of the trainer.
- Inverso Gesellschaft fur innovative Versicherungssoftware mbH
Course - Docker, Kubernetes and OpenShift for Developers
Hands-on exercises to reinforce the concepts.
