Remove Data Drift Remove LLM Remove Metadata
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RAG vs Fine-Tuning for Enterprise LLMs

Towards AI

legal document review) It excels in tasks that require specialised terminologies or brand-specific responses but needs a lot of computational resources and may become obsolete with new data. For instance, a medical LLM fine-tuned on clinical notes can make more accurate recommendations because it understands niche medical terminology.

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The Sequence Pulse: The Architecture Powering Data Drift Detection at Uber

TheSequence

Created Using Midjourney In case you missed yesterday’s newsletter due to July the 4th holiday, we discussed the universe of in-context retrieval augmented LLMs or techniques that allow to expand the LLM knowledge without altering its core architecutre. Like any large tech company, data is the backbone of the Uber platform.

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Top MLOps Tools Guide: Weights & Biases, Comet and More

Unite.AI

This is not ideal because data distribution is prone to change in the real world which results in degradation in the model’s predictive power, this is what you call data drift. There is only one way to identify the data drift, by continuously monitoring your models in production.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. Can you compare images?

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LLMOps: What It Is, Why It Matters, and How to Implement It

The MLOps Blog

TL;DR LLMOps involves managing the entire lifecycle of Large Language Models (LLMs), including data and prompt management, model fine-tuning and evaluation, pipeline orchestration, and LLM deployment. Prompt-response management: Refining LLM-backed applications through continuous prompt-response optimization and quality control.