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コース概要
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
要求
- Experience with Azure Machine Learning
Audience
- Data Scientists
14 時間
お客様の声 (5)
それはまさに私たちが求めていたものであり、参加した社内エンジニアのさまざまなプロフィールをカバーする非常にバランスのとれた量のコンテンツと演習でした。
Arturo Sanchez - INAIT SA
コース - Microsoft Azure Infrastructure and Deployment
Machine Translated
I've got to try out resources that I've never used before.
Daniel - INIT GmbH
コース - Architecting Microsoft Azure Solutions
The Exercises
Khaled Altawallbeh - Accenture Industrial SS
コース - Azure Machine Learning (AML)
とてもフレンドリーで親切
Aktar Hossain - Unit4
コース - Building Microservices with Microsoft Azure Service Fabric (ASF)
Machine Translated
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
