Remove 2023 Remove Clean Data Remove Supervised Learning
article thumbnail

Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI

ODSC - Open Data Science

A recent report by Cloudfactory found that human annotators have an error rate between 7–80% when labeling data (depending on task difficulty and how much annotators are paid). Learn more about the data-centric AI techniques that power Cleanlab at our upcoming talk at ODSC East 2023.

ML 88
article thumbnail

NLP, Tools and Technologies and Career Opportunities

Women in Big Data

The event was part of the chapter’s technical talk series 2023. The Technical Talk Series focuses on Technical Skills, bringing awareness about a technical topic, sharing knowledge, and ways to learn/enhance required skills, thus linking it to career development. We are hearing about NLP, LLMs, ChatGPT and Generative AI a lot !

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Retrieval augmented generation (RAG): a conversation with its creator

Snorkel AI

Alex Ratner spoke with Douwe Keila, an author of the original paper about retrieval augmented generation (RAG) at Snorkel AI’s Enterprise LLM Summit in October 2023. Their conversation touched on the applications and misconceptions of RAG, the future of AI in the enterprise, and the roles of data and evaluation in improving AI systems.

AI 52
article thumbnail

Retrieval augmented generation (RAG): a conversation with its creator

Snorkel AI

Alex Ratner spoke with Douwe Keila, an author of the original paper about retrieval augmented generation (RAG) at Snorkel AI’s Enterprise LLM Summit in October 2023. Their conversation touched on the applications and misconceptions of RAG, the future of AI in the enterprise, and the roles of data and evaluation in improving AI systems.

AI 52
article thumbnail

Take advantage of AI and use it to make your business better

IBM Journey to AI blog

Building and training foundation models Creating foundations models starts with clean data. This includes building a process to integrate, cleanse, and catalog the full lifecycle of your AI data. A hybrid multicloud environment offers this, giving you choice and flexibility across your enterprise.

AI 98