NVMe, or Non-Volatile Memory Express, is a protocol released in 2011 for accessing high-speed storage systems media that came popular with the introduction of Solid-State Drives (SSDs). But what is NVMe and why is it important for machine learning and analytics applications? As businesses and systems continue to consume and process increasingly more data to support manufacturing, it is important to rethink how this data is captured, preserved, accessed and transformed. And because of the speed, NVMe is revolutionizing how the user configures deep learning systems for data storage, data access, and overall architecture when combined with other more traditional storage methods. This article will explain what NVMe is and share a deep technical dive into how the storage architecture works.

Data centers that support AI and ML deployments rely on Graphics Processing Unit (GPU)-based servers to power their computationally intensive architectures. Across multiple industries, expansion in GPU use is behind the over 31 percent CAGR in GPU servers projected through 2024. That means more system architects will be tasked to assure top performance and cost-efficiency from GPU systems. Yet optimizing storage for these GPU-based AI/ML workloads is no small feat. GPU servers are highly efficient for the matrix multiplication and convolution required to train large AI/ML datasets.

When it comes to data, AI is like Pac-Man. Hard disk drives, NAS, conventional data center and cloud-based storage schemes can't sate AI's voracious appetite for speed and capacity, especially for real time. Playing the game today requires a fundamental rethinking of storage as a foundation of machine learning, deep learning, image processing, and neural network success. "AI and Big Data are dominating every aspect of decision-making and operations," says Jeff Denworth, vice president of products and co-founder at Vast Data, a provider of all-flash storage and services. "The need for vast amounts of fast data are rendering the traditional storage pyramid obsolete. Applying new thinking to many of the toughest problems helps simplify the storage and access of huge reserves of data, in real time, leading to insights that were not possible before."

Artificial intelligence (AI) applications are inherently data-intensive, with multiple reads and writes to the file system. And, at the outset, the AI algorithm absorbs tremendous amounts of training data as it learns the parameters of its job. Once that is done, your AI system then diligently performs its task, but it has to output the results somewhere. And, as AI applications scale, they can encounter storage-related bottlenecks that can harm performance. So, at every stage in the deployment, training and operation of AI systems, storage is a big consideration.

As the adoption of artificial intelligence (AI), deep learning, and big data analytics continues to grow, it is becoming increasingly important for edge computing systems to process large data sets in a timely and efficient manner. The basic compute, storage and networking capabilities are all present today at the edge, but speeds and capacity will only continue to increase and advancements like NVMe (Non Volatile Memory Express) will offer significant performance advantages and boost AI adoption at the edge. Edge-based AI: Are We There Yet? It is possible, and becoming easier, to run AI and machine learning with analytics at the edge today, depending on the size and scale of the edge site and the particular system being used. While edge site computing systems are much smaller than those found in central data centers, they have matured, and now successfully run many workloads due to an immense growth in the processing power of today's x86 commodity servers.

Edge, cloud, artificiall intelligence (AI), machine learning, intelligent automation, software-defined…once "emerging" technology trends that have all now become pillars of virtually every conversation about today's IT infrastructure and its evolving needs. In fact, the data center is now at the "core" of a distributed infrastructure spanning the edge and cloud that has organizations of all sizes and in all markets driving towards IT transformation to realize better business outcomes made possible in today's digital information age. Data exists everywhere – generated by seemingly every "thing." The Internet of Things has driven new innovation in both hardware, device and cloud compute – creating new IT requirements for how that data is secured, managed, stored, analyzed at the edge, core and the cloud. And we're just getting started…it's what I've been calling "Lots of Data Everywhere," or L.O.D.E.

At the Dell Technologies World, the company continued its flash first, NVMe end-to-end, storage approach with its new products. The company was also incorporating Artificial Intelligence (AI) capabilities into its server and storage products. Michael Dell-led an interesting announcement of new server and storage products that included applications for Dell products such as for New Jersey-based AeroFarms factory farms and a doctor from USC talking about using Dell-based VR for treatment of PTSD. Dell introduced its Dell EMC PowerMax, which the company said it built for mission-critical applications and is engineered with end-to-end NVMe, ready for Storage Class Memory (SCM) and NVMe over Fabrics (NVMe-oF). . Dell says that they have a seamless migration path from the older VMAX product.

Dell Technologies is positioning its servers, storage systems, hyperconverged infrastructure, and future architecture for workloads such as artificial intelligence, machine learning, and predictive analytics. At Dell Technologies World, the company rolled out a series of data center building blocks -- Dell EMC PowerMax storage systems, updated PowerEdge servers and VxRail appliances, and VXRack SDDC Systems -- and Kinetic, a composable infrastructure. Hewlett Packard Enterprise has a similar effort called Synergy. Ravi Pendekanti, senior vice president of product management and marketing for Dell EMC's server and infrastructure systems group, said the new workload environment "has to be about more than point products." Why it matters, and how we use it What is hyperconvergence?

At the Dell Technologies World the company continued its flash first, NVMe end-to-end, storage approach with its new products. The company was also incorporating Artificial Intelligence (AI) capabilities into its server and storage products. Michael Dell led an interesting announcement of new server and storage products that included applications for Dell products such as for New Jersey based AeroFarms factory farms and a doctor from USC talking about using Dell-based VR for treatment of PTSD. Dell introduced its Dell EMC PowerMax, which the company said it built for mission-critical applications and is engineered with end-to-end NVMe, ready for Storage Class Memory (SCM) and NVMe over Fabrics (NVMe-oF). . Dell says that they have a seamless migration path from the older VMAX product.

I have to be honest. Much of what I predict to be significant for the data storage industry in 2018 may go unnoticed by most IT professionals, even though some of these concern major progress that hasn't been experienced in nearly a full generation! So much has been happening in the development of exciting new storage-class memory (SCM) that will help make arrays faster and more efficient in processing general purpose workloads, and this year I believe we'll see a tangible payoff with the mainstreaming of this technology. This big news is all about Non-Volatile Memory Express (NVMe) and the emergence of this highly anticipated interface within commercial storage arrays. With Artificial Intelligence and Machine Learning seemingly all the rage in IT these days, I expect to see emerging and increasingly practical use cases coming to commercial storage offerings that attempt to further automate storage management, taking concepts such as intelligent data placement and performance tuning to the next level.