Course Outline
Introduction
- Kubeflow on Azure vs. On-Premise vs. Other Public Cloud Providers for Government
Overview of Kubeflow Features and Architecture for Government
Overview of the Deployment Process for Government
Activating an Azure Account for Government
Preparing and Launching GPU-Enabled Virtual Machines 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 AKS for Government
Staging the Training and Validation Data for Government
Configuring Kubeflow Pipelines for Government
Launching a Training Job 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.
- Familiarity with cloud computing principles.
- A general knowledge of containerization (Docker) and orchestration (Kubernetes).
- Some experience 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 within public sector environments.
- Infrastructure engineers looking to integrate machine learning model deployment into their workflows for government initiatives.
- Software engineers seeking to automate the integration and deployment of machine learning features with their applications for government use.
Testimonials (4)
I've got to try out resources that I've never used before.
Daniel - INIT GmbH
Course - Architecting Microsoft Azure Solutions
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
The practical part, I was able to perform exercises and to test the Microsoft Azure features
Alex Bela - Continental Automotive Romania SRL
Course - Programming for IoT with Azure
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.
