Alation

SEPTEMBER 21, 2021

Modern data environments are highly distributed, diverse, and dynamic, many different data types are being managed in the cloud and on-premises, in many different data management technologies, and data is continuously flowing and changing – not unlike traffic on a highway. The Rise of Gen-D and DataOps.

DataOps 52

DataOps Data Governance Data Pipeline 52

Iguazio

JULY 22, 2024

Challenges to Operationalizing Gen AI Building a gen AI or AI application starts with the demo or proof of concept (PoC) phase. The integrated solution allows customers to streamline data processing and storage, ensuring Gen AI applications reach production while eliminating risks, improving performance and enhancing governance.

AI 132

AI AI ML ML 132

DataRobot Blog

SEPTEMBER 13, 2022

Platforms like DataRobot AI Cloud support business analysts and data scientists by simplifying data prep, automating model creation, and easing ML operations ( MLOps ). These features reduce the need for a large workforce of data professionals. Driving Innovation with AI: Getting Ahead with DataOps and MLOps.

Data Scientist 52

Data Scientist ML ML AI 52

Alation

MAY 31, 2023

American Family Insurance: Governance by Design – Not as an Afterthought Who: Anil Kumar Kunden , Information Standards, Governance and Quality Specialist at AmFam Group When: Wednesday, June 7, at 2:45 PM Why attend: Learn how to automate and accelerate data pipeline creation and maintenance with data governance, AKA metadata normalization.

Data Governance 52

Data Governance DataOps Data Pipeline Business Intelligence 52

BAIR

FEBRUARY 17, 2024

We frequently see this with LLM users, where a good LLM creates a compelling but frustratingly unreliable first demo, and engineering teams then go on to systematically raise quality. Operation: LLMOps and DataOps. AI applications have always required careful monitoring of both model outputs and data pipelines to run reliably.

AI 145

AI AI DataOps Data Pipeline 145

BAIR

FEBRUARY 18, 2024

We frequently see this with LLM users, where a good LLM creates a compelling but frustratingly unreliable first demo, and engineering teams then go on to systematically raise quality. Operation: LLMOps and DataOps. AI applications have always required careful monitoring of both model outputs and data pipelines to run reliably.

AI 40

AI AI DataOps Data Pipeline 40

Precisely

JULY 15, 2024

The move to productize data also requires a way to package data products so they are easily and uniformly discoverable and consumed. Data products must be properly designed and organized to be reused across the organization. Standardizing naming conventions also makes data products interoperable.

Data Governance 72

Data Governance DataOps Data Silos Data Pipeline 72