A "AI & ML" Look Ahead for 2020 Feb. 14, 2020

from The Cloudcast·

Sam Charrington (@samcharrington, Host of TWIML & AI Podcast) talks about AI & ML trends in 2020, frameworks to understand usage patterns, hot new technology to explore, how long projects take to succeed, and the inherent bias built into every AI & ML model. SHOW: 437 SHOW SPONSOR LINKS: * 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 * MongoDB Homepage - The most popular database for modern applications * MongoDB Atlas - MongoDB-as-a-Service on AWS, Azure and GCP …



Sam Charrington (@samcharrington, Host of TWIML & AI Podcast) talks about AI & ML trends in 2020, frameworks to understand usage patterns, hot new technology to explore, how long projects take to succeed, and the inherent bias built into every AI & ML model.

SHOW: 437

SHOW SPONSOR LINKS:

SHOW NOTES:

  • TWIML Homepage (Podcasts, eBooks, etc.)
  • TWIML Conference Homepage
  • Sam Charrington on The Cloudcast in 2019 (Eps.321)

    Topic 1 - Welcome back to the show. Let’s start with the broad set of TWIML activities that you’re working on these days.

    Topic 2 - You focus on AI & ML every week, across a lot of different domains and usages. It’s a broad scope. If you had to focus it on Enterprise/Business leaders, how do you structure a conversation around how to align business opportunity and technology choices?

    Topic 3 - What are some of the most commonly used technologies being deployed around AI/ML systems? Any big shifts over the last couple of years?

    Topic 4 - You’ve been around Cloud Computing and DevOps communities, which required companies to go through some people/process change to achieve success. What are the people/process changes that you typically see with AI/ML environments?

    Topic 5 - If somebody asked you how they can put a timeline on when they’ll see value around their AI/ML, is that a realistic ask? What are the factors that go into achieving success in AI/ML projects?

    Topic 6 - What are some of the interesting usages of AI/ML that you’ve seen in use recently?

    Topic 7 - There has been quite a bit of discussion recently about bias in AI/ML algorithms. Can you explain what this means and how it could impact the system’s decision making?

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