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