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When Will We Train Once and Learn Forever? Insights from Dev Rishi, CEO and co-founder @Predibase
What it actually takes to build models that improve over time. In this episode, I sit down with Devvret Rishi, CEO and co-founder of Predibase, to talk about the shift from static models to continuous learning loops, the rise of reinforcement fine-tuning (RFT), and why the real future of enterprise AI isn’t chatty generalists – it’s focused, specialized agents that get the job done.
We cover:
The real meaning behind "train once, learn forever"
How RFT works (and why it might replace traditional fine-tuning)
What makes inference so hard in production
Open-source model gaps—and why evaluation is still mostly vibes
Dev’s take on agentic workflows, intelligent inference, and the road ahead
If you're building with LLMs, this conversation is packed with hard-earned insights from someone who's doing the work – and shipping real systems. Dev is super structural! I really enjoyed this conversation.
Did you like the video? You know what to do:
📌 Subscribe for more deep dives with the minds shaping AI.
Leave a comment if you have something to say.
Like it if you liked it.
That’s it.
Oh yeap, one more thing: Thank you for watching and sharing this video. We truly appreciate you.
Guest:
Devvret Rishi, co-founder and CEO at Predibase
https://predibase.com/
If you don’t see a transcript, subscribe to receive our edited conversation as a newsletter: https://www.turingpost.com/subscribe
Chapters:
00:00 - Intro
00:07 - When Will We Train Once and Learn Forever?
01:04 - Reinforcement Fine-Tuning (RFT): What It Is and Why It Matters
03:37 - Continuous Feedback Loops in Production
04:38 - What's Blocking Companies From Adopting Feedback Loops?
05:40 - Upcoming Features at Predibase
06:11 - Agentic Workflows: Definition and Challenges
08:08 - Lessons From Google Assistant and Agent Design
08:27 - Balancing Product and Research in a Fast-Moving Space
10:18 - Pivoting After the ChatGPT Moment
12:53 - The Rise of Narrow AI Use Cases
14:53 - Strategic Planning in a Shifting Landscape
16:51 - Why Inference Gets Hard at Scale
20:06 - Intelligent Inference: The Next Evolution
20:41 - Gaps in the Open Source AI Stack
22:06 - How Companies Actually Evaluate LLMs
23:48 - Open Source vs. Closed Source Reasoning
25:03 - Dev’s Perspective on AGI
26:55 - Hype vs. Real Value in AI
30:25 - How Startups Are Redefining AI Development
30:39 - Book That Shaped Dev’s Thinking
31:53 - Is Predibase a Happy Organization?
32:25 - Closing Thoughts
Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Semenova explores how intelligent systems are built – and how they’re changing how we think, work, and live.
Sign up: Turing Post: https://www.turingpost.com
FOLLOW US
Devvret and Predibase:
https://devinthedetail.substack.com/
https://www.linkedin.com/company/predibase/
Ksenia and Turing Post:
https://x.com/TheTuringPost
https://www.linkedin.com/in/ksenia-se
https://huggingface.co/Kseniase
View all episodes
By Turing PostWhat it actually takes to build models that improve over time. In this episode, I sit down with Devvret Rishi, CEO and co-founder of Predibase, to talk about the shift from static models to continuous learning loops, the rise of reinforcement fine-tuning (RFT), and why the real future of enterprise AI isn’t chatty generalists – it’s focused, specialized agents that get the job done.
We cover:
The real meaning behind "train once, learn forever"
How RFT works (and why it might replace traditional fine-tuning)
What makes inference so hard in production
Open-source model gaps—and why evaluation is still mostly vibes
Dev’s take on agentic workflows, intelligent inference, and the road ahead
If you're building with LLMs, this conversation is packed with hard-earned insights from someone who's doing the work – and shipping real systems. Dev is super structural! I really enjoyed this conversation.
Did you like the video? You know what to do:
📌 Subscribe for more deep dives with the minds shaping AI.
Leave a comment if you have something to say.
Like it if you liked it.
That’s it.
Oh yeap, one more thing: Thank you for watching and sharing this video. We truly appreciate you.
Guest:
Devvret Rishi, co-founder and CEO at Predibase
https://predibase.com/
If you don’t see a transcript, subscribe to receive our edited conversation as a newsletter: https://www.turingpost.com/subscribe
Chapters:
00:00 - Intro
00:07 - When Will We Train Once and Learn Forever?
01:04 - Reinforcement Fine-Tuning (RFT): What It Is and Why It Matters
03:37 - Continuous Feedback Loops in Production
04:38 - What's Blocking Companies From Adopting Feedback Loops?
05:40 - Upcoming Features at Predibase
06:11 - Agentic Workflows: Definition and Challenges
08:08 - Lessons From Google Assistant and Agent Design
08:27 - Balancing Product and Research in a Fast-Moving Space
10:18 - Pivoting After the ChatGPT Moment
12:53 - The Rise of Narrow AI Use Cases
14:53 - Strategic Planning in a Shifting Landscape
16:51 - Why Inference Gets Hard at Scale
20:06 - Intelligent Inference: The Next Evolution
20:41 - Gaps in the Open Source AI Stack
22:06 - How Companies Actually Evaluate LLMs
23:48 - Open Source vs. Closed Source Reasoning
25:03 - Dev’s Perspective on AGI
26:55 - Hype vs. Real Value in AI
30:25 - How Startups Are Redefining AI Development
30:39 - Book That Shaped Dev’s Thinking
31:53 - Is Predibase a Happy Organization?
32:25 - Closing Thoughts
Turing Post is a newsletter about AI's past, present, and future. Publisher Ksenia Semenova explores how intelligent systems are built – and how they’re changing how we think, work, and live.
Sign up: Turing Post: https://www.turingpost.com
FOLLOW US
Devvret and Predibase:
https://devinthedetail.substack.com/
https://www.linkedin.com/company/predibase/
Ksenia and Turing Post:
https://x.com/TheTuringPost
https://www.linkedin.com/in/ksenia-se
https://huggingface.co/Kseniase