Course Outline
Introduction
- Comparison of Kubeflow on AWS, On-Premise, and Other Public Cloud Providers
Overview of Kubeflow Features and Architecture for Government
Activating an AWS Account for Government
Preparing and Launching GPU-Enabled AWS Instances for Government
Setting up User Roles and Permissions for Government
Preparing the Build Environment for Government
Selecting a TensorFlow Model and Dataset for Government
Packaging Code and Frameworks into a Docker Image for Government
Setting up a Kubernetes Cluster Using EKS for Government
Staging the Training and Validation Data for Government
Configuring Kubeflow Pipelines for Government
Launching a Training Job using Kubeflow in EKS for Government
Visualizing the Training Job in Runtime for Government
Cleaning up After the Job Completes for Government
Troubleshooting for Government
Summary and Conclusion for Government
Requirements
- An understanding of machine learning concepts for government applications.
- Knowledge of cloud computing principles.
- A general understanding of containerization (Docker) and orchestration (Kubernetes).
- Familiarity with Python programming is beneficial.
- Experience working in a command-line environment.
Audience
- Data science engineers for government projects.
- DevOps engineers interested in deploying machine learning models in government settings.
- Infrastructure engineers interested in integrating machine learning models within government systems.
- Software engineers seeking to incorporate and deploy machine learning features in their applications for government use.
Testimonials (4)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
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
All good, nothing to improve
Ievgen Vinchyk - GE Medical Systems Polska Sp. Z O.O.
Course - AWS Lambda for Developers
IOT applications
