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
Testimonials (4)
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 Sanchez - INAIT SA
Course - Microsoft Azure Infrastructure and Deployment
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Course - Azure Machine Learning (AML)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
The quality of the explanations and the large number of topics covered
Hugo SECHIER - Expleo France
Course - Kubeflow on AWS
Machine Translated
