Microsoft this week announced a series of new Azure services and developer technologies that put advanced capabilities spanning artificial intelligence, mixed reality, internet of things and blockchain in the hands of developers. These services range from creating a new interface for a tool that automates the process model creation, to a new no-code visual interface for building, training and deploying models. Reportedly, Microsoft is also going to release Jupyter-like notebooks for advanced users.
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Scott Guthrie, executive vice president, Microsoft Cloud and AI Group, said in a statement, “It’s an incredible time to be a developer. From building AI and mixed reality into apps to leveraging blockchain for solving commercial business problems, developers’ skillsets and impact are growing rapidly. Today we’re delivering innovative Azure services for developers to build the next generation of apps. With 95% of Fortune 500 customers running on Azure, these innovations can have far-reaching impact.”
The company will share these and additional advancements in hybrid cloud and edge computing at its Microsoft Build conference to empower developers.
Here’s the list of announcements that the company has made so far:
- Azure Cognitive Services power applications to see, hear, respond, translate, reason and more. Microsoft is launching a new Cognitive Services category, called “Decision,” that delivers users a specific recommendation for more informed and efficient decision-making. This category includes Content Moderator, the recently announced Anomaly Detector, and a new service called Personalizer, which uses reinforcement learning to provide users with a specific recommendation to enable quick and informed decision-making.
- Microsoft is bringing AI to Azure Search with the general availability of the cognitive search capability, enabling customers to apply Cognitive Services algorithms to extract new insights from their structured and unstructured content. In addition, we are previewing a new capability that enables developers to store AI insights gained from cognitive search, making it easier to create knowledge-rich experiences leveraging Power BI visualizations or machine learning models.
Azure Machine Learning:
- MLOps capabilities with Azure DevOps integration provides developers with reproducibility, auditability and automation of the end-to-end machine learning lifecycle.
- Automated ML advancements and an intuitive UI make developing high-quality models easier.
- Visual machine learning interface provides no-code model creation and deployment experience with drag-and-drop capabilities.
- To enable extremely low latency and cost-effective inferencing, Microsoft is announcing the general availability of hardware–accelerated models that run on FPGAs, as well as ONNX Runtime support for NVIDIA TensorRT and Intel nGraph for high-speed inferencing on NVIDIA and Intel chipsets.