Organization are quickly embracing Artificial Intelligence (AI), Machine Learning and Deep Learning to open new opportunities and accelerate business growth. AI Workloads, however, require massive compute power and has led to the proliferation of GPU acceleration in addition to traditional CPU power. This has led to a break in the traditional data center architecture and amplification of organizational silos, poor utilization and lack of agility. While virtualization technologies have proven themselves in the enterprise with cost effective, scalable and reliable IT computing, Machine Learning infrastructure however has not evolved and is still bound to dedicating physical resources to optimize and reduce training times. Bitfusion helps enterprises dis-aggregate the GPU compute and dynamically attach GPUs anywhere in the datacenter just like attaching storage.

When you buy an item on a favored website, does the site show you pictures of what others have bought? Retailers have been building such systems for years, many built using the programming language R. For older implementations of recommender systems, it's time to consider improving performance and scalability by moving these systems to the cloud --the Azure cloud. Recently, we were asked to help a customer improve the performance and process surrounding the R implementation of their recommender solution and host the model in Azure. Many of their early analytic products were built in R, and they wanted to preserve that investment. After a review of their solution, we identified bottlenecks that could be vanquished.

Powerful and cost-effective HPC platforms promote data fusion, reduce training time, and enable ultra-scale real-time data analytics to power deep learning systems. In today's digital climate, organizations of every size and industry are both collecting and generating enormous amounts of data that can potentially be used to solve the world's greatest problems--from national security and fraud detection to scientific breakthroughs and technological advancement. However, traditional analysis techniques and practices are not capable of rapidly delivering automated, real-time insights from the rising data volumes to the point that artificial intelligence (AI) is becoming vital to harnessing the full understanding of scientific and business data. The evolution of Big Data is driving a major paradigm shift in the field of AI, which is increasing the need for high performance computing (HPC) technologies that can support high performance data analytics (HPDA). According to an IDC report, the HPDA server market is projected to grow at a 26% CAGR through 2020, including an additional $3.9 billion in revenue by 2018.