AI for the Mainstream June 3, 2020

from The Cloudcast·

Venkat Rangan (Co-Founder & CTO @ Clari) talks about AI and application into more mainstream areas and revenue generation. SHOW: 453 SHOW SPONSOR LINKS: * Taos Homepage * Taos - Gartner MQ - Cloud Professional Services * Studio 3T - Homepage * Studio 3T - 30 Day Free Trial * Datadog Homepage - Modern Monitoring and Analytics * Try Datadog yourself by starting a free, 14-day trial today. Listeners of this podcast will also receive a free Datadog T-shirt CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotw PodCTL Podcast is Back (Enterprise Kubernetes) - http://podctl.com SHOW NOTES: * Clari website



Venkat Rangan (Co-Founder & CTO @ Clari) talks about AI and application into more mainstream areas and revenue generation.

SHOW: 453

SHOW SPONSOR LINKS:

PodCTL Podcast is Back (Enterprise Kubernetes) - http://podctl.com

SHOW NOTES:

  • Clari website

    Topic 1 - Venkat, you have a very interesting background as a technologist in our industry. Give everyone a brief introduction and if you don’t mind also tell everyone a bit about being a member of the Forbes Technology Council. I believe you are our first guest from there.

    Topic 2 - Today we’re going to dig back into AI as a topic. Let’s start at the start. In your current role you have been involved in technology over the years and Clari has been around for awhile. What compelled you to focus on this space?

    Topic 3 - AI has suffered from a perception issue. It often is seen as unapproachable with a pretty high barrier to entry. We’ve spoken to companies in the past about specific industry or vertical applications (manufacturing for instance). Many architects and our listeners are practitioners vs. data scientists. How do we solve this problem and “bring AI to mainstream”

    Topic 4 - In order for that to happen, what does our industry need? Is it SaaS based, services from public clouds, etc?

    Topic 5 - Having been in field sales for years, it’s always been somewhere between complete and utter guesswork all the way to precision, rocket science level analysis. - What are your thoughts here and how can AI improve this situation?

    Topic 6 - What’s next for industry? What are the use cases both today and in the future?

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