Azure Machine Learning

Use an enterprise-grade service for the end-to-end machine learning lifecycle.

Overview

Azure Machine Learning is a cloud service for accelerating and managing the machine learning project lifecycle. Machine learning professionals, data scientists, and engineers can use it in their day-to-day workflows: Train and deploy models, and manage MLOps. You can create a model in Azure Machine Learning or use a model built from an open-source platform, such as Pytorch, TensorFlow, or scikit-learn. MLOps tools help you monitor, retrain, and redeploy models.

Learning

Learning Paths

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Learning Modules

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News

11/16/2022, Azure Podcast
Sujit and Evan are joined by Amir Dahan, Senior Product Manager for Networking at Microsoft, to discuss Azure DDOS protection. Media File: Edpisode447.mp3 YouTube:...
New public preview features include reading Delta Lakes in fewer steps, debugging and monitoring training jobs, and performing data wrangling.
11/9/2022, GA Announcements
New GA features include the ability to automate auto-shutdown/auto-start schedules, configure and customize a compute instance, seamlessly build NLP/vision models, and assess AI systems.
10/12/2022, Preview Announcements
Features include functionality to promote pipelines and models across workspaces, perform data wrangling at scale, shorten training times, and lower set-up costs with Azure Container for PyTorch.
10/12/2022, GA Announcements
Features include controlling/customizing a model’s training code and using Python functions to develop tasks.
10/12/2022, Azure Podcast
The team catches up with the developers of the Databricks Accelerator for Azure Purview to learn when, where, and why you might use it.   Media...
10/3/2022, GA Announcements
New features include the ability to establish event-driven notifications and the capability to label data in text documents using text named-identity recognition.
Includes the ability to control access to sensitive data, the capability to contrast differences to assess their performance, and the functionality to stop idle compute instances automatically.
Mathias Brandewinder enjoys solving challenging business problems with software engineering and applied mathematics techniques, and some creativity. His current focus is on functional programming...
8/17/2022, GA Announcements
Hierarchical forecasting, now generally available, offers you the capability to produce consistent forecasts for all levels of your data.
New features now available in public preview include AutoML Code Generation enhancements and AutoML in Pipelines.
Damian Brady is a Developer Advocate at GitHub. He's a developer, speaker, and author specializing in DevOps, MLOps, developer process, and software architecture. Formerly a Cloud Advocate at...
6/22/2022, GA Announcements
New features include MLflow enhancements and train and deploy models in Azure hybrid and multi-cloud.