Closing the Loop Between AI Training and Inference with Lin Qiao - #742

Closing the Loop Between AI Training and Inference with Lin Qiao - #742

In this episode, we're joined by Lin Qiao, CEO and co-founder of Fireworks AI. Drawing on key lessons from her time building PyTorch, Lin shares her perspective on the modern generative AI development lifecycle. She explains why aligning training and inference systems is essential for creating a seamless, fast-moving production pipeline, preventing the friction that often stalls deployment. We explore the strategic shift from treating models as commodities to viewing them as core product assets. Lin details how post-training methods, like reinforcement fine-tuning (RFT), allow teams to leverage their own proprietary data to continuously improve these assets. Lin also breaks down the complex challenge of what she calls "3D optimization"—balancing cost, latency, and quality—and emphasizes the role of clear evaluation criteria to guide this process, moving beyond unreliable methods like "vibe checking." Finally, we discuss the path toward the future of AI development: designing a closed-loop system for automated model improvement, a vision made more attainable by the exciting convergence of open and closed-source model capabilities. The complete show notes for this episode can be found at https://twimlai.com/go/742.

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Why Models Are AI’s Next Training Dataset with Damian Borth - #772

Why Models Are AI’s Next Training Dataset with Damian Borth - #772

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How AI Learns to Smell with Alex Wiltschko - #771

How AI Learns to Smell with Alex Wiltschko - #771

In this episode, Alex Wiltschko, founder and CEO of Osmo, joins the show to discuss his goal of giving computers a sense of smell and what it takes to build olfactory intelligence. We explore the sci...

8 Heinä 59min

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Is RAG Dead? Lessons from Building AI for Tax Law with Alex Bowcut - #769

Is RAG Dead? Lessons from Building AI for Tax Law with Alex Bowcut - #769

As context windows grow into the millions of tokens, many AI practitioners are questioning whether retrieval-augmented generation (RAG) is still necessary. If modern models can ingest entire libraries...

9 Kesä 51min

Relational Foundation Models for Enterprise Data with Jure Leskovec - #768

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In this episode, Jure Leskovec, co-founder and chief scientist at Kumo and professor of computer science at Stanford, joins us to explore two fronts of his work: AI for science and relational deep lea...

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How to Find the Agent Failures Your Evals Miss with Scott Clark - #767

How to Find the Agent Failures Your Evals Miss with Scott Clark - #767

In this episode, Scott Clark, co-founder and CEO of Distributional, joins us to explore how teams can reliably operate and improve complex LLM systems and agents in production. Scott introduces a Masl...

7 Touko 53min

How to Engineer AI Inference Systems with Philip Kiely - #766

How to Engineer AI Inference Systems with Philip Kiely - #766

In this episode, Philip Kiely, head of AI education at Baseten, joins us to unpack the fast-evolving discipline of inference engineering. We explore why inference has become the stickiest and most cri...

30 Huhti 54min

How Capital One Delivers Multi-Agent Systems with Rashmi Shetty - #765

How Capital One Delivers Multi-Agent Systems with Rashmi Shetty - #765

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