Microsoft adds enterprise support for PyTorch AI on Azure
Microsoft is stepping up its support for enterprise customers that are using Facebook's PyTorch deep-learning framework on the Microsoft Azure cloud.
Microsoft has been contributing to the open-source PyTorch project in various ways, including taking over the the Windows 10 PyTorch library last year to boost GPU-accelerated machine learning training on Windows 10's Subsystem for Linux (WSL).
Like Google's TensorFlow, PyTorch is a library for the Python programming language — a favorite for machine learning and AI — that integrates with important Python add-ons like NumPy and data-science tasks that require faster GPU processing.
Microsoft uses PyTorch internally and it's become a very popular project on Microsoft-owned GitHub.
Microsoft took over the PyTorch library because it needed some love from Microsoft on Windows 10 since that version lagged the capability of libraries available for Linux and macOS.
With a year under Microsoft, the company is rolling out PyTorch Enterprise on the Azure cloud. This will give Microsoft Premier and Unified Support for Enterprise customers additional benefits, such as prioritized requests, hands-on support, and solutions for hotfixes, bugs and security patches. The aim is to give PyTorch users a more reliable production experience.
This is in the name of supporting Azure machine learning and attracting developers who might otherwise be tempted by Amazon Web Service's PyTorch offerings.
"Microsoft customers with Microsoft Premier and Unified support using PyTorch are automatically eligible for PyTorch Enterprise and can request hotfixes. These requests will be prioritized, quickly addressed and deployed in the long-term support version of PyTorch, also available in Azure Machine Learning," Microsoft said in a press release.
ZDNet's Mary Jo Foley has additional coverage of Microsoft's Azure AI services for building AI applications.
Microsoft is announcing that developers with no machine learning experience can access vision, speech, language, and decision-making AI models through API calls. Machine learning developers can also build their models with key tooling, including Python Jupyter notebooks, Microsoft's Visual Studio Code (VS Code) code editor, PyTorch and TensorFlow.