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Course Outline
Introduction
- Kubeflow on Azure vs on-premise vs on 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
- An understanding of machine learning concepts.
- Knowledge of cloud computing concepts.
- A general understanding of containers (Docker) and orchestration (Kubernetes).
- Some Python programming experience is helpful.
- Experience working with a command line.
Audience
- Data science engineers.
- DevOps engineers interesting in machine learning model deployment.
- Infrastructure engineers interested in machine learning model deployment.
- Software engineers wishing to automate the integration and deployment of machine learning features with their application.
28 Hours
Testimonials (5)
It was very much what we asked for—and quite a balanced amount of content and exercises that covered the different profiles of the engineers in the company who participated.
Arturo Sánchez - INAIT SA
Course - Microsoft Azure Infrastructure and Deployment
The Exercises
Khaled Altawallbeh - Accenture Industrial SS
Course - Azure Machine Learning (AML)
very friendly and helpful
Aktar Hossain - Unit4
Course - Building Microservices with Microsoft Azure Service Fabric (ASF)
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.
