IBM Journey to AI blog

OCTOBER 30, 2024

This solution supports the validation of adherence to existing obligations by analyzing governance documents and controls in place and mapping them to applicable LRRs. The enhanced metadata supports the matching categories to internal controls and other relevant policy and governance datasets.

Prompt Engineer 285

Prompt Engineer Prompt Engineering ETL Machine Learning 285

Flipboard

OCTOBER 25, 2023

Text, images, audio, and videos are common examples of unstructured data. Most companies produce and consume unstructured data such as documents, emails, web pages, engagement center phone calls, and social media. Therefore, there is a need to being able to analyze and extract value from the data economically and flexibly.

ML 165

ML Metadata AI AI 165

IBM Journey to AI blog

JULY 3, 2024

Open is creating a foundation for storing, managing, integrating and accessing data built on open and interoperable capabilities that span hybrid cloud deployments, data storage, data formats, query engines, governance and metadata.

Metadata 113

Metadata Data Quality AI AI 113

AWS Machine Learning Blog

MAY 31, 2024

Challenges and considerations with RAG architectures Typical RAG architecture at a high level involves three stages: Source data pre-processing Generating embeddings using an embedding LLM Storing the embeddings in a vector store. Vector embeddings include the numeric representations of text data within your documents.

Generative AI 131

Generative AI Machine Learning Artificial Intelligence Artificial Intelligence 131

Snorkel AI

JULY 5, 2023

Generally, data is produced by one team, and then for that to be discoverable and useful for another team, it can be a daunting task for most organizations. Even larger, more established organizations struggle with data discovery and usage. AR : Yeah. PP : Yeah, I think you guys are spot on.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

Generally, data is produced by one team, and then for that to be discoverable and useful for another team, it can be a daunting task for most organizations. Even larger, more established organizations struggle with data discovery and usage. AR : Yeah. PP : Yeah, I think you guys are spot on.

Large Language Models 52

Large Language Models Metadata AI Machine Learning 52

Snorkel AI

JULY 5, 2023

Generally, data is produced by one team, and then for that to be discoverable and useful for another team, it can be a daunting task for most organizations. Even larger, more established organizations struggle with data discovery and usage. AR : Yeah. PP : Yeah, I think you guys are spot on.

Large Language Models 52

Large Language Models Metadata AI Machine Learning 52