Course Outline
Introduction
- Kubeflow on AWS vs. On-Premises vs. Other Public Cloud Providers for Government
Overview of Kubeflow Features and Architecture
Activating an AWS Account for Government Use
Preparing and Launching GPU-Enabled AWS Instances for Government Projects
Setting up User Roles and Permissions for Secure Access
Preparing the Build Environment for Compliance with Government Standards
Selecting a TensorFlow Model and Dataset for Government Applications
Packaging Code and Frameworks into a Docker Image for Government Use
Setting up a Kubernetes Cluster Using EKS for Enhanced Security
Staging the Training and Validation Data for Efficient Processing
Configuring Kubeflow Pipelines for Government Workflows
Launching a Training Job using Kubeflow in EKS for Government Projects
Visualizing the Training Job in Runtime for Transparent Monitoring
Cleaning up After the Job Completes to Ensure Data Integrity and Compliance
Troubleshooting Common Issues in Government Deployments
Summary and Conclusion
Requirements
- An understanding of machine learning concepts for government applications.
- Knowledge of cloud computing principles for government use.
- A general understanding of containerization (Docker) and orchestration (Kubernetes) for government environments.
- Some experience with Python programming is beneficial.
- Familiarity with command-line operations for government systems.
Audience
- Data science engineers for government agencies.
- DevOps engineers interested in deploying machine learning models within government frameworks.
- Infrastructure engineers interested in implementing machine learning model deployments for government operations.
- Software engineers seeking to integrate and deploy machine learning features in government applications.
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
