Machine Learning Street Talk (MLST) cover image

Machine Learning Street Talk (MLST)

Cohere's SVP Technology - Saurabh Baji

Sep 12, 2024
Saurabh Baji, SVP of Engineering at Cohere, shares insights from his journey in deploying large language models for business. He explains how Cohere focuses on pragmatic models tailored for enterprises, emphasizing flexible deployment options like cloud and on-premises solutions. Highlighting the importance of retrieval-augmented generation, he details how models can leverage enterprise data. Baji predicts a surge in AI adoption in the coming months, stressing the significance of trust and security in enterprise applications.
01:30:25

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Podcast summary created with Snipd AI

Quick takeaways

  • Cohere prioritizes creating efficient, pragmatic language models specifically designed for enterprise use rather than focusing solely on model size.
  • The company offers various flexible deployment options, ensuring that enterprise clients can choose between cloud services and on-premises installations to meet their needs.

Deep dives

Evaluating the Value of Language Models for Businesses

Businesses are grappling with whether language models (LMs) truly meet their needs or if the initial excitement around them is waning. While there is significant potential for LMs to provide value, their actual application in practical scenarios is crucial. The implementation of LMs must be executed in a way that yields real benefits for businesses, rather than merely being a technological novelty. Establishing a clear connection between LMs and tangible business outcomes is essential for their sustained relevance in the marketplace.

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