Course Outline
Introduction
- Introduction to Kubernetes
- Overview of Kubeflow Features and Architecture
- Kubeflow on AWS compared to on-premise versus other public cloud providers
Cluster Setup using AWS EKS
Cluster Setup on-Premise using Microk8s
Deployment of Kubernetes via a GitOps Approach
Data Storage Strategies
Creation of a Kubeflow Pipeline
Pipeline Initiation
Definition of Output Artifacts
Storage of Metadata for Datasets and Models
Hyperparameter Tuning with TensorFlow
Visualization and Analysis of Results
Multi-GPU Training
Establishment of an Inference Server for ML Model Deployment
Utilization of JupyterHub
Networking and Load Balancing
Auto Scaling of a Kubernetes Cluster
Troubleshooting
Summary and Conclusion
Requirements
- Understanding of Python syntax
- Practical experience with Tensorflow, PyTorch, or similar machine learning frameworks
- Active AWS account with required resources
Target 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.
