IBM Journey to AI blog

OCTOBER 12, 2023

But the implementation of AI is only one piece of the puzzle. The tasks behind efficient, responsible AI lifecycle management The continuous application of AI and the ability to benefit from its ongoing use require the persistent management of a dynamic and intricate AI lifecycle—and doing so efficiently and responsibly.

Data Scientist 234

Data Scientist Metadata Responsible AI Explainability 234

The MLOps Blog

JUNE 27, 2023

Data storage and versioning You need data storage and versioning tools to maintain data integrity, enable collaboration, facilitate the reproducibility of experiments and analyses, and ensure accurate ML model development and deployment. Easy collaboration, annotator management, and QA workflows.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Unite.AI

JULY 5, 2024

Accountability and Transparency: Accountability in Gen AI-driven decisions involve multiple stakeholders, including developers, healthcare providers, and end users. Transparent, explainable AI models are necessary for informed decision-making. This data provides insights into the social factors that influence patient health.

Data Integration 147

Data Integration Data Quality Algorithm AI Modeling 147

Pickl AI

DECEMBER 10, 2024

As the global AI market, valued at $196.63 from 2024 to 2030, implementing trustworthy AI is imperative. This blog explores how AI TRiSM ensures responsible AI adoption. Key Takeaways AI TRiSM embeds fairness, transparency, and accountability in AI systems, ensuring ethical decision-making.

Explainability 52

Explainability AI AI Artificial Intelligence 52