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Shattering the Myths of GenAI: Interview with Forrester Analyst Rowan Curran

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Manage episode 396407218 series 3279398
Content provided by Domino Data Labs. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Domino Data Labs or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
The biggest challenges to driving impact with AI have little to do with AI and everything to do with humans. Nowhere is this greater than with GenAI where myths and misconceptions abound as to how organizations should be designing, developing and operationalizing GenAI-based applications. In this episode with Rowan Curran, industry analyst at Forrester Research, we debunk the most harmful myths and discuss how AI teams are shattering these myths and delivering transformative outcomes.
Join us as we discuss:
  • The role of data scientists and ML engineers in GenAI projects
  • Successful approaches to prompt engineering
  • The linkages between MLOps and LLMOps
  continue reading

80 episodes

Artwork
iconShare
 
Manage episode 396407218 series 3279398
Content provided by Domino Data Labs. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Domino Data Labs or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
The biggest challenges to driving impact with AI have little to do with AI and everything to do with humans. Nowhere is this greater than with GenAI where myths and misconceptions abound as to how organizations should be designing, developing and operationalizing GenAI-based applications. In this episode with Rowan Curran, industry analyst at Forrester Research, we debunk the most harmful myths and discuss how AI teams are shattering these myths and delivering transformative outcomes.
Join us as we discuss:
  • The role of data scientists and ML engineers in GenAI projects
  • Successful approaches to prompt engineering
  • The linkages between MLOps and LLMOps
  continue reading

80 episodes

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