Great teams incorporate a variety of skill sets. For example, a football team consisting of 11 quarterbacks would get crushed in a game against talented linemen, running backs and receivers. It's no different when building a team for an enterprise AI project; you can't just throw a bunch of data scientists into a room and expect them to come up with a revenue-generating or efficiency-improving project without support from other members of the enterprise. Interestingly, many companies do just that, creating a disconnect between data science teams and IT/DevOps when it comes to AI development. This gap is a significant reason why AI pilot projects fail.

Digital transformation is responsible for artificial intelligence workloads being created at an unprecedented scale. These workloads require corporations to collect and store mountains of data. Even as business intelligence is being extracted from current machine learning models, new data inflows are being used to create new models and update existing models. Building AI models is complex and expensive. It is also very much different than traditional software development.

IoT growth lags projections because most endpoint devices are constrained, fixed-function platforms with customized embedded software stacks communicating over dedicated, non-interoperable networks. AI-capable SoCs, converged platforms, and interoperable networks address these growth barriers, transforming IoT devices from underpowered embedded gadgets to scalable compute nodes that use modern software tools and DevOps. This fundamental change to IoT economics plays out over several years, but we'll look back on 2022 as the start of the IoT plug-and-play era. Note: Moor Insights & Strategy writers and editors may have contributed to this article. Moor Insights & Strategy, like all research and tech industry analyst firms, provides or has provided paid services to technology companies. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking, or speaking sponsorships.

As a tech analyst, one of the particularly exciting topics I get to cover is the march towards fully autonomous vehicles. I've long projected that these next generation vehicles, ranging from semi-to-fully autonomous, will be one of the most impactful developments we see in our lifetimes. As with many nascent technologies, it's looking like some of the earliest applications will be in the commercial sector. Trucking, specifically, stands to benefit from new levels of autonomy, promising gains in efficiency, safety, comfort and sustainability. For several years now, I've been closely watching Plus (formerly known as Plus.ai), a pioneer in self-driving truck technology.

Many organizations leverage cloud-based HPC resources to develop and test AI models, and then move the production models to on-premises systems -- for lots of good reasons. For enterprises looking to gain a competitive edge with artificial intelligence systems, it's off to the races. Everyone is trying to get there first, which means that everyone has a need for speed in their software development and testing processes. This sense of urgency is one of the reasons organizations increasingly look to the cloud for fast access to flexible pools of high performance computing resources. For example, if your organization wants to train a neural network to drive a recommendation engine or a natural language processing application, you might need access to dozens of compute nodes, some accelerators, fast interconnects and high-speed storage.

We are just 18 days away from The Next AI Platform event on March 10, 2020 at The Glasshouse in San Jose. Remember, this sold out last year. If you haven't already registered make sure to do so as soon as possible to avoid getting closed out of the unique PowerPoint-free, live-interview and hosted panel day focused on what's next for large-scale AI infrastructure (this year's emphasis is on inference in particular). Below is the tentative agenda. As with all events, it is subject to last minute changes but this is the confirmed lineup as of this morning.

Karl Freund is an HPC and AI analyst at Moor Insights. In this video from the NVIDIA booth at SC17, Karl Freund from Moor Insights presents: How AI is Reshaping HPC. "Researchers have begun putting Machine Learning to work solving problems that do not lend themselves well to traditional numerical analysis, or that require unaffordable computational capacity. This talk with discuss three primary approaches being used today, and will share some case studies that show significant promise of lower latency, improved accuracy, and lower cost." Karl Freund has spent his career leading organizations in the server and semiconductor industries, working at HPE, IBM, CRAY, SGI, Calxeda, and AMD.

These new Vega GPUs look like they have sufficient performance to be in the same ballpark as's highly acclaimed family of PASCAL GPUs. Taken together with the optimized ROCm ML software AMD has developed, they form a solid first step for market entry. But it will all come down to the ROCm open source software to transform the hardware's potential performance into real value. AMD announced the Radeon Instinct MI6, MI8, and the Vega-based MI25 for Machine Learning back in December, with Vega shipments expected mid-2017. Vega is being positioned for compute-intensive training jobs against the NVIDIA Pascal-based Tesla P100 with what looks like competitive performance potential.