LLMs in the Trenches: What Breaks and Why (w/ Andriy Burkov)
Description
LLMs seem like a hot solution now, until you try deploying one.
In this episode, Andriy Burkov, machine learning expert and author of The Hundred-Page Machine Learning Book, joins us for a grounded, sometimes blunt conversation about why many LLM applications fail.
We talk about sentiment analysis, difficulty with taxonomy, agents getting tripped up on formatting, and why MCP might not solve your problems.
If you're tired of the hype and want to understand the real state of applied LLMs, this episode delivers.
What You'll Learn:
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What is often misunderstood about LLMs
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The reliability of sentiment analysis
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How can we make agents more resilient?
📚 Check out Andriy's books on machine learning and LLMs:
The Hundred-Page Machine Learning Book
The Hundred-Page Language Models Book: hands-on with Pytorch
Register for free to be part of the next live session: https://bit.ly/3XB3A8b
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