Cognitive AI: Intel’s Multimodal AI Systems | Performance or Productivity in AI | Intel Innovation

Vasudev Lal talks about Cognitive AI: Intel’s Multimodal AI Systems Optimized on Intel hardware

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.

Development tools and resources help you prepare, build, deploy, and scale your AI solutions: https://intel.ly/3MywYEK

Public Dataset Leaderboards: https://bit.ly/3fZeUal

Leaderboards: https://bit.ly/3Vk543f

WebQA: https://bit.ly/3CzBHkD

Intel Innovation 2022: https://intel.ly/3CSsW6O

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Cognitive AI: Intel’s Multimodal AI Systems | Performance or Productivity in AI | Intel Innovation 2022