At its Build conference, Microsoft said Project Brainwave, its deep learning acceleration platform harnessing Intel FPGAs, has been integrated with Azure Machine Learning for the cloud as well as edge computing.
Insights: Machine Learning and Artificial Intelligence
Machine learning and artificial intelligence have arrived in the data center, changing the face of the hyperscale server farm as racks begin to fill with ASICs, GPUs, FPGAs and supercomputers. The race to leverage machine learning is led by the industry’s marquee names, including Google, Facebook and IBM. As usual, the battlefield runs through the data center, with implications for the major cloud platforms and chipmakers like Intel and NVIDIA.
Data center rack density is trending higher, prompted by growing adoption of powerful hardware to support artificial intelligence applications. Some industry observers see a growing opportunity for specialists in high-density hosting.
NVIDIA today introduced beefier new GPUs, along with a new interconnect fabric to accelerate workloads, and an initiative to extend machine learning capabilities to smartphones and Internet of Things (IoT) devices.
In our Executive Roundtable, four data center executives discuss the growing use of use of artificial intelligence in data center management.
In the near term, a lot of new infrastructure in the data center is being devoted to enabling AI software applications to run. In this week’s Voices of the Industry column, Marc Cram, Director of Sales for Server Technology, explores applications for the evolving world of AI, including the variety of software tools designed to find hidden patterns and correlations between elements of large data sets.
CBRE Data Centre Solutions is using artificial intelligence technology from LitBit to automate the management of data center facilities. CBRE manages more than 800 data centers around the world.
In this Voices of the Industry column, Patrick Quirk, Vice President and General Manager of Management Systems at Vertiv, explores the connection between upskilled employees and the autonomous data center. By combining data, connectivity and machine learning, data centers can bridge the skills gap, prepare for changing environments and loads, and eventually implement predictive capabilities.
In 2017, Microsoft also focused on hyperscale hardware, continuing its innovation with FPGAs and GPUs to speed its networking and machine learning services, while test-driving ARM cloud servers.
During 2017, Facebook began building bigger data center campuses, applying more compute horsepower to AI, and using servers to warm homes.
The rapid growth of AI is contributing to the building of new services, as well as enhancing products already on the market. And this trend has direct design implications for data centers. Find out how the expanding AI industry could change the face of colocation.