Sponsored Artificial intelligence (AI) promises to revolutionize healthcare. The underlying combination of Machine Learning and analytics can process medical data sets so large and medical images so numerous that they are beyond the scale of researchers, physicians and staff. In so doing, this AI duo promises to help identify patients at risk and prevent the onset of diseases and medical conditions. For existing patients, the hope is AI can identify hidden illnesses, pinpoint medical problems and in the development and application of treatments that assist patient recovery. Yet adoption has been held back thanks to the cost and complexity of building and owning the kinds of high-performance systems needed.

AI computing needs high levels of data processing and conventional AI systems function by transmitting data to a cloud server to be processed. Insights about the data and the decisions to be taken by the system are then transmitted back to connected devices. This approach works fine but for the rapidly increasing number of IoT devices, this is not ideal. There are issues both with the processing power, cloud connectivity and battery capacities in the mobile devices. While connected devices are not ideal to support large data crunching, sometimes they are designed for purposes that need insights in real-time, such as in self-driving cars or in anomaly detection systems.

You already know that machine learning is essentially a form of data analytics, but where did it come from and how has it evolved to become what it is today? In the past couple of decades, we have seen a rapid expansion and evolution of information technology. In 1995, data storage cost around $1000/GB; by 2014 that cost had plummeted to $0.03/GB (2). With access to larger and larger data sets, data scientists have made major advances in neural networks, which have led to better accuracy in modeling and analytics. As we mentioned earlier, the combination of data and analytics opens up unique opportunities for businesses. Now that machine learning is entering the mainstream, the next step along the path is predictive analytics, which goes above and beyond previous analytics capabilities.

TensorFlow* is a leading deep learning and machine learning framework, which makes it important for Intel and Google to ensure that it is able to extract maximum performance from Intel's hardware offering. This paper introduces the Artificial Intelligence (AI) community to TensorFlow optimizations on Intel Xeon and Intel Xeon Phi processor-based platforms. These optimizations are the fruit of a close collaboration between Intel and Google engineers announced last year by Intel's Diane Bryant and Google's Diane Green at the first Intel AI Day. We describe the various performance challenges that we encountered during this optimization exercise and the solutions adopted. We also report out performance improvements on a sample of common neural networks models.

Since most of us need more than a "machine learning only" server, I'll focus on the reality of how Intel Xeon SP Platinum processors remain the best choice for servers, including servers needing to do machine learning as part of their workload. Here is a partial run down of key software for accelerating deep learning on Intel Xeon Platinum processor versions enough that the best performance advantage of GPUs is closer to 2X than to 100X. There is also a good article in Parallel Universe Magazine, Issue 28, starting on page 26, titled Solving Real-World Machine Learning Problems with Intel Data Analytics Acceleration Library. High-core count CPUs (the Intel Xeon Phi processors – in particular the upcoming "Knights Mill" version), and FPGAs (Intel Xeon processors coupled with Intel/Altera FPGAs), offer highly flexible options excellent price/performance and power efficiencies.

You already know that machine learning is essentially a form of data analytics, but where did it come from and how has it evolved to become what it is today? In the past couple of decades, we have seen a rapid expansion and evolution of information technology. In 1995, data storage cost around $1000/GB; by 2014 that cost had plummeted to $0.03/GB (2). With access to larger and larger data sets, data scientists have made major advances in neural networks, which have led to better accuracy in modeling and analytics. As we mentioned earlier, the combination of data and analytics opens up unique opportunities for businesses.

You already know that machine learning is essentially a form of data analytics, but where did it come from and how has it evolved to become what it is today? In the past couple of decades, we have seen a rapid expansion and evolution of information technology. In 1995, data storage cost around 1000/GB; by 2014 that cost had plummeted to 0.03/GB (2). With access to larger and larger data sets, data scientists have made major advances in neural networks, which have led to better accuracy in modeling and analytics. As we mentioned earlier, the combination of data and analytics opens up unique opportunities for businesses.

You already know that machine learning is essentially a form of data analytics, but where did it come from and how has it evolved to become what it is today? In the past couple of decades, we have seen a rapid expansion and evolution of information technology. In 1995, data storage cost around 1000/GB; by 2014 that cost had plummeted to 0.03/GB (2). With access to larger and larger data sets, data scientists have made major advances in neural networks, which have led to better accuracy in modeling and analytics. As we mentioned earlier, the combination of data and analytics opens up unique opportunities for businesses.

You already know that machine learning is essentially a form of data analytics, but where did it come from and how has it evolved to become what it is today? In the past couple of decades, we have seen a rapid expansion and evolution of information technology. In 1995, data storage cost around 1000/GB; by 2014 that cost had plummeted to 0.03/GB (2). With access to larger and larger data sets, data scientists have made major advances in neural networks, which have led to better accuracy in modeling and analytics. As we mentioned earlier, the combination of data and analytics opens up unique opportunities for businesses.