Introduction | Performance or Productivity in AI | Intel Innovation 2022

Kavitha Prasad Intro – In this video, Kavitha Prasad, VP & GM of Datacenter & AI Execution & Strategy, Intel, talks about challenges with self-managed, self-built systems/pipelines and developers having to deal with myriad of choices. She touches on deriving business outcomes via a full spectrum of AI applications, the heterogenous & continuous nature of AI workloads and what Intel is doing to help developers build and scale AI systems seamlessly.

To move AI from research/innovation to mainstream, there are, even today, significant barriers that exist to both build and deploy models to scale. To develop and deploy models, a data scientist/AI developer is doing all kinds of tasks that are tangential to data science but less actual data science. The chart you’re looking at reflects research we did at our own Mlcon machine learning conference and surveys of our own customers. All of these incidental tasks are necessary at some level, but they all add friction to the process, delay time to value, and contribute to lost opportunities.
So what are some of these incidental tasks? Installing and configuring hardware like GPUs, CPUs, accelerators, and storage, standing up cloud computing resources, configuring containers and container orchestration software like Kubernetes, figuring out how to manage hybrid cloud infrastructures. All of this reduces the ROI of AI from a business perspective. There are tools that help reduce these complexities, but few have extensive MLOps capabilities, and they often add complexity with heavy code requirements or require expertise in tools like Kubernetes or Docker to take advantage of containerization for model standardization, management, and repeatability.

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Introduction | Performance or Productivity in AI | Intel Innovation 2022