From automation and data analysis to internal communications and customer service, adopting artificial intelligence (AI) into your enterprise can enhance your entire operation.
In this Tech We Like, we’re looking at technologies that can help your enterprise get the most out of AI. Most of these technologies are from our partners at Dell as part of its Server Accelerators products. Let’s get into the details.
OpenShift already provides you with the flexibility to utilize different development tools (CI/CD pipelines, UIs, IDEs, etc.), and when coupled with Kubeflow, your Kubernetes workflows can easily move from traditional applications developers to ML and AI developers.
Dell’s PowerEdge servers support both GPUs and FPGAs, and for AI, each has their own uses.
Adding GPUs to your PowerEdge server allows you to do more deep learning training rather than traditional ML clustering. Basically, this means you can do more complicated learning on large data sets for things like facial recognition and image classification.
FPGAs, on the other hand, can be added once you’ve deployed your trained ML and AI models into production and want to run them at an accelerated rate. For example, if you want sensors within a car bumper to report instantaneously whether something passing in front of the vehicle poses a danger.
When building out large-scale training infrastructures, one of the most common bottlenecks is storage. Dell’s partnership with NVIDIA has produced this reference architecture to build out scalable deep learning pods to help you efficiently break through these bottlenecks.
By incorporating NVIDIA DGX servers and GPUs, as well as the all-flash Isilon NAS array, the architecture keeps your data close to the box and allows you to utilize your more expense GPUs for the projects that need them.
Looking to gain more clarity on AI adoption? Download our free eBook, The Enterprise Guide To Kicking Off the AI Adoption Process.
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