O'Reilly on Data

OCTOBER 19, 2021

Let’s start by considering the job of a non-ML software engineer: writing traditional software deals with well-defined, narrowly-scoped inputs, which the engineer can exhaustively and cleanly model in the code. Not only is data larger, but models—deep learning models in particular—are much larger than before.

IT 364

IT Testing Experimentation Software 364

IBM Big Data Hub

JANUARY 9, 2024

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. In the generative AI or traditional AI development cycle, data ingestion serves as the entry point.

Enterprise 71

Enterprise Data Integration Data Quality Contextual Data 71

Alation

JUNE 28, 2022

Collaborate more effectively: Break down data silos for better understanding of data assets across all business units. Create taxonomies and modeling languages: Enrich data analytics by enhancing relationships between data for ensuring consistent modeling outcomes when new data is introduced.

Metadata 52

Metadata Management Data Governance Machine Learning 52