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Course Outline
Introduction
MLOps Overview
- What is MLOps?
- MLOps in Azure Machine Learning architecture
Preparing the MLOps Environment
- Setting up Azure Machine Learning
Model Reproducibility
- Working with Azure Machine Learning pipelines
- Bridging Machine Learning processes with pipelines
Containers and Deployment
- Packaging models into containers
- Deploying containers
- Validating models
Automating Operations
- Automating operations with Azure Machine Learning and GitHub
- Retraining and testing models
- Rolling out new models
Governance and Control
- Creating an audit trail
- Managing and monitoring models
Summary and Conclusion
Requirements
- Experience with Azure Machine Learning
Audience
- Data Scientists
14 Hours
Testimonials (4)
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.
