Azure Machine Learning

Azure Stream Analytics is a fully managed Platform as a Service (PaaS) that supports thousands of mission-critical customer applications powered by real-time insights.

Azure Machine Learning is the center for all things machine learning on Azure, be it creating new models, deploying models, managing a model repository and/or automating the entire CI/CD pipeline for machine learning.

We are excited to share the winners of the first Microsoft Azure AI Hackathon, hosted on Devpost.

Today, Alysa Taylor, Corporate Vice President of Business Applications and Industry, announced several new AI-driven insights applications for Microsoft Dynamics 365.

Artificial intelligence (AI) workloads include megabytes of data and potentially billions of calculations. With advancements in hardware, it is now possible to run time-sensitive AI workloads on the edge while also sending outputs to the cloud for downstream applications.

Congratulations to the PyTorch community on the release of PyTorch 1.2! Last fall, as part of our dedication to open source AI, we made PyTorch one of the primary, fully supported training frameworks on Azure.

Today we are announcing the open sourcing of our recipe to pre-train BERT (Bidirectional Encoder Representations from Transformers) built by the Bing team, including code that works on Azure Machine Learning, so that customers can unlock the power of training custom versions of BERT-large models

This year at Microsoft Build 2019, we announced a slew of new releases as part of Azure Machine Learning service which focused on MLOps. These capabilities help you automate and manage the end-to-end machine learning lifecycle.

When data scientists work on building a machine learning model, their experimentation often produces lots of metadata: metrics of models you tested, actual model files, as well as artifacts such as plots or log files.

Data scientists have a dynamic role. They need environments that are fast and flexible while upholding their organization’s security and compliance policies. Notebook Virtual Machine (VM), announced in May 2019, resolves these conflicting requirements while simplifying the overall experience for data scientists.

Read the latest announcements about Azure Maps, our machine-learning tools, how we give clinicians the right data, and more.

Build more accurate forecasts with the release of capabilities in automated machine learning. Have scenarios that require have gaps in training data or need to apply contextual data to improve your forecast or need to apply lags to your features?