Azure Machine Learning

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


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.

Key Resources

Newsfeed: RSS

Getting Started

  1. 12/13/2022, Learning Module
    Azure Machine Learning provides your software with automated data identification and extraction from your documents. Using an SDK or REST, the AI-powered service decreases, or...


Date News
Our April highlight includes Azure OpenAI Service, ChatGPT and open source related technologies to help you better understand the impact they have on today’s development landscape. Start learning...
In this episode we chat with Andrés Padilla and Meer Alam about practical use of AI and ML systems.  Examples of this are discussed around autonomous drone / delivery models and the various...
New features now available in GA include the ability to visualize timeseries models, and create a Compute Instance on behalf of another user.
New feature now available in Public Preview includes the ability to receive troubleshooting documentation on failed environment builds and shorten the training phase of large-scale distributed...
Azure Machine Learning is now Generally Available in the UK West region.
Are you an open source developer who wants to learn how to build intelligent apps, and with other, likeminded people in your country or region?     To help bring open source communities across...
We’re excited to share that Microsoft has been recognized as a Leader in the IDC MarketScape Worldwide Machine Learning Operations (MLOps) Platforms 2022 Vendor Assessment.
If you have ever taken any training modules or learned something new by going through the official documentation, the material you used was probably written or co-authored by one or many...
New features in GA include the ability to place customized tags, search for machine learning assets, isolate network for managed online endpoints, build custom metrics views, and simplify data...
New features now available in Public Preview include the ability to build an end-to-end training pipeline with no-code, recover a deleted workspace, and identify root causes of pipeline failure.
Summary Microsoft Azure Data Explorer is a great resource to ingest and process streaming data. Azure Data Lake Storage is a great resource for storing large amounts of data. The end-to-end...
Dr. Kapfhammer and his team focus on research related to flaky software tests, which are tests that produce inconsistent or unpredictable results. Why are tests flaky? How can we identify them...
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.
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.
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.
Features include controlling/customizing a model’s training code and using Python functions to develop tasks.
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...
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.