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MLOps vs. LLMOps Panel // LLMs in Conference in Production Conference Part II

Sep 1, 2023
35:48
Snipd AI
In this podcast, the MLOps vs. LLMOps Panel discuss the high-level differences between MLOps and LLMOps, the impact of ML ops on companies, the challenges of open source tools and data safety in financial firms, the cost and rationalization of MLOps, options for large enterprises in ML model development, and the use of foundational models and vector databases.
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Podcast summary created with Snipd AI

Quick takeaways

  • LOM Ops allows companies to leverage large language models as a base and fine-tune them for specific use cases, offering faster deployment and prompt engineering in comparison to traditional ML models.
  • Vector stores and feature stores play a crucial role in LOM Ops, enabling better context and observability for language models, while evaluation and debugging become more challenging as model complexity increases.

Deep dives

The Rise of LOM Ops

LOM Ops, which stands for Language Model Operations, is gaining momentum as a powerful tool for enterprises. It is seen as a way to leverage large language models (LLMs) like OpenAI to boost productivity and improve outcomes. LOM Ops offers the ability to start with a generalized LLM as a base and then fine-tune or specialize it for specific use cases. This approach allows companies to achieve faster deployment and iterate on prompt engineering rather than relying solely on traditional ML models. The stack for LOM Ops includes elements like vector stores and feature stores, which enable better context and observability for the models. Evaluation and debugging are crucial challenges in LOM Ops, as the complexity and interdependencies of models increase. Enterprises are grappling with the decision of whether to rely on mature models like OpenAI or build custom models to ensure data privacy. Cost rationalization is also a consideration, and startups are emerging to provide cost-effective LOM Ops solutions. While LOM Ops offers tremendous potential, it is still in its early stages, and the full impact on ML Ops remains to be seen.

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