Subject archive for "model-management"

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

Model Management

High-standard ML validation with Deepchecks

We've blogged before about the importance of model validation, a process that ensures that the model is performing the way it was intended and that it solves the problem it was designed to solve. Validations and tests are key elements to building machine learning pipelines you can trust. We've also talked about incorporating tests in your pipeline, which many data scientists find problematic. The issues stem from the fact that not all data scientists feel confident about traditional code testing methods, but more importantly, data science is so much more than just code. When validating pipelines we need to think about verifying the data integrity, inspecting its distributions, validating data splits, model evaluation, model comparison etc. But how can we deal with such complexity and maintain consistency in our pipelines? Enter Deepchecks - an open-source Python package for testing and validating machine learning models and data.

By Noam Bressler14 min read

Perspective

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

Perspective

4 Ways to Maintain Machine Learning Model Accuracy

Algorithms may be the toast of today’s high-performance technology races, but sometimes proponents forget that, like cars, models also need a regular tune-up. A highly visible and catastrophic AI model failure recently shamed Zillow, the online real estate company that was forced to shutter its home-buying business.

By Josh Poduska7 min read

Model Management

A Guide to Machine Learning Model Deployment

Machine-learning (ML) deployment involves placing a working ML model into an environment where it can do the work it was designed to do. The process of model deployment and monitoring takes a great deal of planning, documentation and oversight, and a variety of different tools.

By David Weedmark7 min read

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