Course Outline
Introduction
- Introduction to Kubernetes for government
- Overview of Kubeflow Features and Architecture for government
- Kubeflow on AWS vs. On-Premise vs. Other Public Cloud Providers for government
Setting up a Cluster using AWS EKS for government
Setting up an On-Premise Cluster using Microk8s for government
Deploying Kubernetes using a GitOps Approach for government
Data Storage Approaches for government
Creating a Kubeflow Pipeline for government
Triggering a Pipeline for government
Defining Output Artifacts for government
Storing Metadata for Datasets and Models for government
Hyperparameter Tuning with TensorFlow for government
Visualizing and Analyzing the Results for government
Multi-GPU Training for government
Creating an Inference Server for Deploying ML Models for government
Working with JupyterHub for government
Networking and Load Balancing for government
Auto Scaling a Kubernetes Cluster for government
Troubleshooting for government
Summary and Conclusion for government
Requirements
- Familiarity with Python syntax for government projects
- Experience with TensorFlow, PyTorch, or other machine learning frameworks
- An AWS account with the necessary resources for government use
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.
