Course Outline
Introduction
- Kubeflow on Azure compared to on-premise and other public cloud providers
Overview of Kubeflow Features and Architecture
Overview of the Deployment Process
Activating an Azure Account
Preparing and Launching GPU-enabled Virtual Machines
Setting up User Roles and Permissions
Preparing the Build Environment
Selecting a TensorFlow Model and Dataset
Packaging Code and Frameworks into a Docker Image
Setting up a Kubernetes Cluster Using AKS
Staging the Training and Validation Data
Configuring Kubeflow Pipelines
Launching a Training Job
Visualizing the Training Job in Runtime
Cleaning up After the Job Completes
Troubleshooting
Summary and Conclusion
Requirements
- A solid understanding of machine learning concepts.
- Knowledge of cloud computing principles.
- A general familiarity with containers (Docker) and orchestration tools (Kubernetes).
- Some experience with Python programming is advantageous.
- Experience working with a command-line interface.
Target Audience
- Data science engineers.
- DevOps engineers interested in deploying machine learning models.
- Infrastructure engineers focused on machine learning model deployment.
- Software engineers aiming to automate the integration and deployment of machine learning features into their applications.
Testimonials (2)
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.
