Flipboard

MAY 30, 2025

Data quality is ownership of the consuming applications or data producers. Governance The two key areas of governance are model and data: Model governance Monitor model for performance, robustness, and fairness. Model versions should be managed centrally in a model registry.

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phData

JULY 22, 2024

I consciously chose to pivot away from general software development and specialize in Data Engineering. I’ve moved from building user interfaces and backend systems to designing data models, creating data pipelines, and gaining valuable insights from complex datasets.

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Iguazio

DECEMBER 18, 2023

This will require investing resources in the entire AI and ML lifecycle, including building the data pipeline, scaling, automation, integrations, addressing risk and data privacy, and more. By doing so, you can ensure quality and production-ready models. Here’s to a successful 2024! The post What Lays Ahead in 2024?

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Iguazio

FEBRUARY 8, 2024

To read more about LLMOps and MLOps, checkout the O’Reilly book “Implementing MLOps in the Enterprise” , authored by Iguazio ’s CTO and co-founder Yaron Haviv and by Noah Gift. LLMOps (Large Language Model Operations), is a specialized domain within the broader field of machine learning operations (MLOps). What is LLMOps?

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Iguazio

DECEMBER 18, 2023

This will require investing resources in the entire AI and ML lifecycle, including building the data pipeline, scaling, automation, integrations, addressing risk and data privacy, and more. By doing so, you can ensure quality and production-ready models. Here’s to a successful 2024! The post What Lays Ahead in 2024?

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Alation

FEBRUARY 17, 2023

In this blog, I’ll address some of the questions we did not have time to answer live, pulling from both Dr. Reichental’s book as well as my own experience as a data governance leader for 30+ years. Can you have proper data management without establishing a formal data governance program? This is a very good thing.

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DagsHub

AUGUST 29, 2024

Data can change a lot, models may also quickly evolve and dependencies become old-fashioned which makes it hard to maintain consistency or reproducibility. With weak version control, teams could face problems like inconsistent data, model drift , and clashes in their code.

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