Artificial intelligence initiatives are springing up in almost every industry and generating a huge market in their wake. Gartner predicts that AI augmentation will generate $3.9 trillion in business value by 2022 alone. What’s more, Gartner says that AI promises to be the most disruptive class of technologies during the next 10 years, driven by increases in computational power, advances in storage technology, the availability of new data and the ubiquity of deep learning toolkits.
However, despite all of the momentum for AI, turning the promise into business value isn’t as easy as flipping a switch. Organizations making the journey to AI face a multitude of complex choices related to data, skillsets, software stacks, analytic toolkits and infrastructure components. Each of these choices has significant implications for the time to value associated with AI initiatives.
With so much on the line, organizations starting down the path to AI can benefit greatly from the experiences of those who have already made the journey. That’s the case with the two companies featured in a recent webinar that I had the good fortune to host. Those two companies are Personalis and Dell.
Personalis is a young life sciences company devoted to helping cancer patients by enabling the next generation of personalized immuno-oncology therapeutics and diagnostics through rigorous genetics-based innovation. In the webinar, Douglas Zeman II, manager of scientific computing and IT at Personalis, describes the evolution of AI at Personalis to improve and accelerate the delivery of personalized life-saving cancer therapies.
One of the keys to making this evolution at Personalis was the conquering of the company’s data challenges. Personalis had multiple storage architectures and silos, and all the problems that come with that — from multiple storage protocols to scalability challenges. Day-to-day processes involved transferring data among different storage architectures and dealing with multiple storage products and vendors.
To overcome these challenges, Personalis consolidated its data environment into a unified Dell EMC Isilon cluster that handles multiple storage protocols and includes hot and cold tiering capabilities that range from lightning-fast flash to lower-cost spinning disk. This consolidated storage environment has grown from hundreds of terabytes to nearly 10 petabytes in just 18 months and now handles all of the company’s genetic data, from the lab to processing to archiving.
In one particularly exciting advance, the speed of the Isilon all-flash tier allowed Personalis to accelerate data processing — and to accelerate the AI results that patients and clinicians are waiting on. To make this step forward, the company deployed Dell EMC PowerEdge C4140 servers to accelerate processing by 12x. That means that operations that previously took 24 hours can now be completed in just two hours.
Gains like this amount to a huge step forward for AI applications. And that is just one of the lessons learned from a company that has made the journey to AI.
Elsewhere in the webinar, Darryl Smith, chief data platform architect and distinguished engineer at Dell EMC, talks about Dell drinking its own champagne as it matured its data lake into a machine learning AI platform to drive better business outcomes.
To make this transition, Dell went through a stepwise data analytics journey that moved from data consolidation to data enablement to data analytics and ultimately advancing to AI with machine learning and deep learning — all enabled by the same Dell EMC Isilon data lake.
For any organization making the journey to AI, there are great lessons to be learned in the experiences of these two innovative companies. For the full story, attend the on-demand webinar Journey to AI with Dell EMC Isilon.