When will audible cha-chings finally ring out from end-of-the-line data intelligence in action? Businesses are pinning their hopes on artificial intelligence — presently the “holy grail” of big data — to make it happen. What’s the vessel for this golden goose? A cloud-native open-source project or crypto-funded software as a service startup? No, it’s a big metal box of on-premises hardware (with software goodies on top); that’s Dell EMC’s message to the market with its new Ready Solutions for AI.

Dell Technologies Inc.’s own basement is stacked wall-to-wall with hardware and software goods; all that baggage was an advantage in padding out Ready Solutions, according to Tom Burns, senior vice president and general manager of networking and solutions at Dell EMC. The company honed its chops over the years building out all kinds of infrastructure and testing it out on its broad base of customers. Dell EMC hand selected the hardware best suited to AI computing and packaged it inside Ready Solutions.

“We’re bringing this to them in a way which allows them, through a service-provisioning portal, to basically set up and get to work much faster,” Burns said.

Burns spoke with Peter Burris (@plburris), host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, at the Dell Ready System for AI Launch Event in Austin, Texas. They discussed Ready Solutions, trends in AI, and finding the fast lane to data-analytics value. (* Disclosure below.)

Previously, many vendors of AI tech handed users a plateful of preliminary steps to execute, Burns pointed out. “There were 15, 20, 25 different steps just to log on, just to get enough automation and enough capability in order to get the information that they need,” he said.

Ready Solutions’ infrastructure, data analytics, and service portal combo whittled the preamble down to about five clicks through a user-friendly graphical user interface with no command line interface required. In five clicks, and with little or no help from IT, data scientists can get to work on solid data analytics instead of wasting time on preparation. “Heaven knows those guys are not cheap,” Burns said.

A pound of piece parts for an ounce of AI

Artificial intelligence is the most powerful technology available for crunching big data. If anything on the market can be said to make producing simple, actionable insights from massive data sets easy, it’s AI. This is why venture capitalists and chief information officers are investing so much money and planning into AI. The influx of VC dollars to AI startups is boosting the U.S. tech market to heights not seen since the dot.com bubble.

Q2’18 was a record quarter for artificial intelligence companies with investments totaling over $2.3 billion, according to PwC and CB Insight’s “MoneyTree Report.” Forty-six percent of CIOs plan to leverage AI technology, according to a Gartner Inc. survey. That the same survey found only four percent had already done so tells us something of what’s involved; synthesizing the skills and technologies needed remains highly challenging for most companies.

“Despite huge levels of interest in AI technologies, current implementations remain at quite low levels,” said Whit Andrews, research vice president and distinguished analyst at Gartner. “However, there is potential for strong growth as CIOs begin piloting AI programs through a combination of buy, build and outsource efforts.”

Caveat emptor: These CIOs may feel tempted to buy tools with flimsy data analytics capabilities that leave users asking, “Is this really AI?“

“Most of my current clients are still working on understanding what it means in their organizations,” said data scientist and consultant Michael Pomatto, during SiliconANGLE Media’s #GetReady4AI CrowdChat. “There are a lot of things being labeled AI that are not.”

Dell EMC tries to net infra, software bee swarm

True AI requires a roundup of infrastructure, software and expertise difficult to cram into a single tool. Dell EMC clasped hands together with a number of partners to make Ready Solutions systems gel. One of the boxes is geared for AI machine learning with Hadoop (the popular open-source big data framework); a second is for AI and deep learning with Nvidia Corp. GPUs. The first is built on Dell EMC’s PowerEdge R640 and R740xd rack servers, which users can outfit with up to two Intel Xeon Scalable central processing units. The software powering the ML includes Cloudera Inc.’s Data Science Workbench, Apache Spark open-source analytics engine, and Dell EMC’s Data Science Provisioning Engine to simplify deployment of Intel Corp.’s BigDL AI extension for Spark.

Dell EMC Ready Solutions for AI and deep learning with NVIDIA puts the much-hyped computational power of GPUs to work. It’s built on the R740xd rack server and the C4140 from the PowerEdge family and can hold up to four Nvidia Tesla V100 GPUs. Compared to CPUs, GPUs have a sky-high core count that makes them superior at running data-intense AI workloads. The system also features servers with Dell EMC’s Isilon F800 network-attached flash storage, partner Bright Computing Inc.’s Cluster Manager for Data Science, and open-source TensorFlow ML framework.

Deep partnerships allowed Dell EMC to integrate all these parts into a specialized AI solution, Garima Kochhar (pictured), systems senior principal engineer at Dell Technologies, told theCUBE. “The tighter that things work together, the better that they work together, and that’s directly through all the technologies that we have in the Dell Technologies umbrella and with Dell EMC,” she said. “That’s because of our super-close relationships with our partners that allow us to build these solutions that are painless for our customers and our users.”

Living on the edge from the comfort of home

The large, complex open-source community around AI is a particularly rich vein to mine, according to Nick Curcuru, vice president of big data analytics at Mastercard International Inc. “Those open-source systems that are out there — how do we learn from that community? It’s that community that allows you to get there,” he said.

Businesses that are trying to get to real AI on their own have a long slog ahead of them, Curcuru added. Mastercard has partnered with Dell EMC to help the company scale out AI technologies it’s applying to biometrics, like fingerprint, facial recognition and more. Even the way someone uses a keyboard or mobile device can serve as a signature. 

“For us, it’s always about scale. How can we roll this across 220 countries? We’re 165 million transactions per hour,” Curcuru said.

Scaling out AI to edge devices is challenging given the logistics and cost of moving data from the core to the edge. AI models are insatiable data-eating monsters; thus, their training must take place in a vast core, whereas resulting edge-device algorithms must ask the right few questions. “We want that algorithm to be smart, so what three to four things do we need that algorithm to be looking for within that artificial intelligence?” Curcuru asked. “Then it goes back into the core and retrieves something, whether that’s your fingerprint, your biometrics, how you’re interacting with that machine, to say, ‘Yes, that’s you; yes, we want that transaction to go through,’ or, ‘No, stop it before it even begins.'” 

The Dell EMC Ready Solutions for AI are about to get an edge-compute upgrade, according to Robert Stober, director of product management at Bright Computing. “Bright Edge will allow [you] to manage all your compute resources — including those at the edge — as a single cluster,” he said during the SiliconANGLE CrowdChat.

Watch the complete video interviews below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the Dell Ready System for AI Launch event. (* Disclosure: TheCUBE is a paid media partner for the Dell Ready System for AI Launch event. Neither Dell EMC, the event sponsor, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE