AI News, Microsoft goes after the barrier to entry for data science with Azure ML

Microsoft goes after the barrier to entry for data science with Azure ML

A month ago, I got a pre-briefing on Microsoft’s Azure Machine Learning with Roger Barga (group program manager, machine learning) and Joseph Sirosh (CVP, Machine Learning).

Yesterday, Microsoft made it available to customers and partners, so now seems like the right time to talk about how it fits into the broader market.

You need to have a deep knowledge of machine learning to get things up and running, whereas Microsoft’s making the effort to curate solid algorithms.

This makes for a nice overlap between Microsoft product and research, the latter of which  has some outstanding examples of machine learning (such as the real-time translation from English to Chinese in late 2012 by Rick Rashid).

Some comparisons of data against various distributions to check the best fit and whether that suits the chosen algorithm would be one approach;

They need to figure out how to tell the right stories to the right audiences about ease of use and flexibility, build broader appeal to both forward-leaning and enterprise audiences, and continue to focus on constructing a larger data-science offering on Azure and on Windows (including partners like Hortonworks).

Microsoft Research Blog

Second, Project Brainwave uses a powerful “soft” DNN processing unit (or DPU), synthesized onto commercially available FPGAs.  A number of companies—both large companies and a slew of startups—are building hardened DPUs.  Although some of these chips have high peak performance, they must choose their operators and data types at design time, which limits their flexibility.  Project Brainwave takes a different approach, providing a design that scales across a range of data types, with the desired data type being a synthesis-time decision.  The design combines both the ASIC digital signal processing blocks on the FPGAs and the synthesizable logic to provide a greater and more optimized number of functional units.  This approach exploits the FPGA’s flexibility in two ways.  First, we have defined highly customized, narrow-precision data types that increase performance without real losses in model accuracy.  Second, we can incorporate research innovations into the hardware platform quickly (typically a few weeks), which is essential in this fast-moving space.

We architected this system to show high actual performance across a wide range of complex models, with batch-free execution.  Companies and researchers building DNN accelerators often show performance demos using convolutional neural networks (CNNs).  Since CNNs are so compute intensive, it is comparatively simple to achieve high performance numbers.  Those results are often not representative of performance on more complex models from other domains, such as LSTMs or GRUs for natural language processing.  Another technique that DNN processors often use to boost performance is running deep neural networks with high degrees of batching.  While this technique is effective for throughput-based architectures—as well as off-line scenarios such as training—it is less effective for real-time AI.  With large batches, the first query in a batch must wait for all of the many queries in the batch to complete.  Our system, designed for real-time AI, can handle complex, memory-intensive models such as LSTMs, without using batching to juice throughput.

Introducing ML.NET : Build 2018

ML.NET is aimed at providing a first class experience for Machine Learning in .NET. Using ML.NET, .NET developers can develop and infuse custom AI into ...

Introduction to Azure ML Services [Part 1/4]

This episode of the AI Show is the first in a series talking about the Azure ML Services. The new Azure Machine Learning Services provide an integrated, ...

What's new with Azure Machine Learning : Build 2018

In September, we launched a huge set of updates for Azure Machine Learning to let you manage the end to end lifecycle of Machine Learning Development.

The improved Azure Log Analytics: A powerful query language with machine learning, and more

Azure Log Analytics is now offering new interactive and expressive query language and advanced analytics portal. The service is now powered by the same ...

Smarter data analysis with JavaScript and Azure ML functions in Excel : Build 2018

Excel continues to be the scratchpad for data analysis everywhere, and with new capabilities for JavaScript-based functions and connections to ML based ...

The Microsoft AI platform - GS07

Join Joseph Sirosh, Corporate Vice President of the Cloud AI Platform, as he dives deep into the latest additions to the Microsoft AI platform and capabilities.

First look at What’s New in Azure Machine Learning

Take in the huge set of capabilities announced at Ignite for the next generation of the Azure Machine Learning platform. Build and deploy ML applications in the ...

BI in the age of artificial intelligence

Equip your organization today for the future of data analytics. See how users of Microsoft Power BI, for example, can experience their data in a natural way by ...

Build Intelligent Apps with the Microsoft Data & AI Platform : Build 2018

Join Rohan Kumar, Corporate Vice President of Data Platform, to learn how Microsoft provides the most comprehensive data platform for your modern, intelligent ...

How to build machine learning applications using R and Python in SQL Server 2017 - BRK3298

Learn how to use R and Python integration in Microsoft SQL Server 2016/2017 to build machine learning applications. Learn how to operationalize machine ...