Azure Machine Learning

Enterprises today are adopting artificial intelligence (AI) at a rapid pace to stay ahead of their competition, deliver innovation, improve customer experiences, and grow revenue.

Over the past few years, we’ve seen incredible transformation across industries as companies harness the power of AI to transform business processes and drive impact for their customers.

Congratulations to the TensorFlow community on the release of TensorFlow 2.0! In this blog, we aim to highlight some of the ways that Azure can streamline the building, training, and deployment of your TensorFlow model.

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.