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How to Practice Data-Centric AI and Have AI Improve its Own Dataset

ODSC - Open Data Science

For more complex issues like label errors, you can again simply filter out all the auto-detected bad data. For instance, when fine-tuning various LLM models on a text classification task (politeness prediction), this auto-filtering improves LLM performance without any change in the modeling code!

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Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning Blog

You can deploy this solution with just a few clicks using Amazon SageMaker JumpStart , a fully managed platform that offers state-of-the-art foundation models for various use cases such as content writing, code generation, question answering, copywriting, summarization, classification, and information retrieval.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Can you see the complete model lineage with data/models/experiments used downstream? Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on. Is it accessible from your language/framework/infrastructure, framework, or infrastructure?

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

The MLOps Blog

LLMOps is key to turning LLMs into scalable, production-ready AI tools. Embeddings are essential for LLMs to understand natural language, enabling them to perform tasks like text classification, question answering, and more.

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List of Groundbreaking and Open-Source Conversational AI Models in the Language Domain

Marktechpost

Based on the transformer architecture, Vicuna is an auto-regressive language model and offers natural and engaging conversation capabilities. The chatbot is designed for conversation and instruction and excels in summarizing, generating tables, classification, and dialog. trillion tokens. scripts, which are available on GitHub.

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Announcing New Tools for Building with Generative AI on AWS

Flipboard

For instance, a financial firm that needs to auto-generate a daily activity report for internal circulation using all the relevant transactions can customize the model with proprietary data, which will include past reports, so that the FM learns how these reports should read and what data was used to generate them.