Thinking about artificial intelligence (AI) infrastructure can feel a bit like finding your way through a maze – winding your way from data collection to solution development, to creating value through new insights, equipped with the right servers, compute, and storage to help you on your way. When designing infrastructure for your AI solutions, a helpful way to navigate the maze of choices is to begin with your end-to-end workflow – from data generation to solution deployment and value creation. Considering your requirements at each stage can make it easier to select the right infrastructure for your needs. We at HPE can help along this journey by offering a broad portfolio of choices in servers and storage so that you can optimize your deployments – from data center to edge. The latest choice comes in the form of the first product server from HPE based on a specialized AI processor: the HPE Edgeline EL8000 platform with the Qualcomm Cloud AI 100 accelerator.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Artificial intelligence (AI) is highly effective at parsing extreme volumes of data and making decisions based on information that is beyond the limits of human comprehension. But it suffers from one serious flaw: it cannot explain how it arrives at the conclusions it presents, at least, not in a way that most people can understand. This "black box" characteristic is starting to throw some serious kinks in the applications that AI is empowering, particularly in medical, financial and other critical fields, where the "why" of any particular action is often more important than the "what." This is leading to a new field of study called explainable AI (XAI), which seeks to infuse AI algorithms with enough transparency so users outside the realm of data scientists and programmers can double-check their AI's logic to make sure it is operating within the bounds of acceptable reasoning, bias and other factors.

HPE ML Ops brings DevOps-like speed and agility to the entire machine learning lifecycle. As enterprises move beyond experimentation to more widespread adoption of AI, a vast majority of them are running into "last mile" issues related to model deployment and management. Gartner predicts that by 2021, at least 50 percent of machine learning models built with the intention of being operationalized will not see the light of day.1 What is "operationalization"? Admittedly, it's a mouthful--and some even abbreviate it as "o16n". But it's the biggest challenge facing enterprises as they embark on the next phase in their AI journey with machine learning (ML). Note: In this blog post, I'll refer primarily to ML, but the same applies to deep learning (DL), a subset of ML.