This post has been republished via RSS; it originally appeared at: New blog articles in Microsoft Tech Community.
To highlight new features, share new customer stories and talk about how we use Azure AI at Microsoft, we are proud to announce the new Azure AI Blog! Expect a lot more information here over the coming weeks and months.
Azure AI is a set of AI services built on Microsoft’s breakthrough innovation from decades of world-class research in vision, speech, language processing, and custom machine learning. With Azure AI, customers get access to the same proven AI capabilities that power Xbox, HoloLens, Bing, and Office 365.
Azure AI helps organizations:
- Develop machine learning models that can help with scenarios such as demand forecasting, recommendations, or fraud detection using Azure Machine Learning.
- Incorporate vision, speech, and language understanding capabilities into AI applications and bots, with Azure Cognitive Services and Azure Bot Service.
- Build knowledge-mining solutions to make better use of untapped information in their content and documents using Azure Search.
For this blog, we will be talking about the Azure AI platform across the board – new technologies, how customers are using it, and groundbreaking research.
To kick it all off, we’d like to talk about a core service in our platform: Azure Machine Learning. In just under a year since it has become generally available, we have seen terrific growth and could not be more impressed with the community that has sprung up around it. The combination of helping to address all the steps of a machine learning (ML) lifecycle along with the ability start training on your local machine and then to the cloud has helped our users realize the power of ML to address their core business needs.
To understand how you might get started with Azure Machine Learning, we suggest you take a quick browse through the documentation, where you can find:
- Machine Learning tutorials
- Azure Machine Learning quick starts and examples
- A how-to guide for training with TensorFlow, Keras, Sci-kit Learn, MXNet or PyTorch
- API and language references
If you’d like a more thorough overview, you can also read the What is Azure Machine Learning summary page.
If you would like to get started, we offer several choices!
- A designer in which you can drag-n-drop modules to build your experiments and then deploy models
- Jupyter notebooks in which you use the SDKs to write your own code, such as these sample notebooks
- Visual Studio Code extension
You can get started with a free trial today!
If you have any questions, comments, or requests, please jump in below.