Subject archive for "model-governance"


Why AI reproducibility is the holy grail of good governance

True reproducibility means anyone can return to a point in time — anywhere in the AI/ML lifecycle — and see how a model was built and understand its purpose and KPIs. Yet, most AI models are built outside of controlled environments and systems of record. Enterprise AI platforms like Domino solve this by automatically unifying and capturing all model provenance and all artifacts across teams, users, tools, and environments without manual detective work, which can produce mixed results.

By Leila Nouri7 min read

Vacuum cleaner vacuums money
Data Science Platform

AI Costs Keeping You Up at Night? It's time for Domino

AI costs rise with data & infrastructure needs. Domino's guide offers advice on cost controls, productivity, governance & risk - to save you money.

By Yuval Zukerman4 min read

Model Governance

Taming Model Sprawl with Domino Model Registry

At Rev4, Domino recently announced the launch of Domino Model Sentry, a tightly integrated set of capabilities for building and operating AI responsibly at scale. With Domino Model Sentry, organizations can closely and continuously manage all aspects of AI throughout the entire lifecycle. This article will focus specifically on a core capability of Domino Model Sentry, Model Registry.

By Tim Law7 min read

cost governance reporting billing savings Kubecost
Cost-Effective Data Science

Domino’s New Cost Governance Capabilities for AI Drive Accountability, Visibility & Savings

Controlling and tracking AI, data science and related compute costs are often manual and error prone, and require tagging specific infrastructure to distributed IT workloads. provides the governance guardrails necessary to reduce and control infrastructure costs, including expensive high-performance compute (e.g. GPUs) resources.

By Nikhil Jethava6 min read


4 Ways to Better Manage and Govern Financial Services and Insurance Models

The financial services industries are starting to realize the full import of the fact that, like household chores like dishwashing and garden work, ML models are never really done. Rather, AI and ML models need to be monitored for validity, and often, they also need to be re-explained and re-documented for regulators. So the spotlight is on model risk management (MRM) and governance (MRG), two related critical processes for financial services and insurance companies, and the importance of these two disciplines is only expected to grow.

By Nathan Greenhut9 min read

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