
Sign up to save your podcasts
Or
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:
CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotw
SHOW NOTES:
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?
FEEDBACK?
4.6
147147 ratings
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:
CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotw
SHOW NOTES:
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?
FEEDBACK?
377 Listeners
272 Listeners
283 Listeners
1,032 Listeners
40 Listeners
593 Listeners
624 Listeners
202 Listeners
110 Listeners
267 Listeners
181 Listeners
189 Listeners
63 Listeners
140 Listeners
52 Listeners