InfoWorld

The importance of monitoring machine learning models

Changing assumptions and ever-changing data mean the work doesn’t end after deploying machine learning models to production. These best practices keep complex models reliable. Agile development teams ...
SiliconANGLE

Fiddler Labs upgrades its AI model monitoring platform to detect new types of bias and drift

Startup Fiddler Labs Inc. said today it’s doing more to help companies ensure their artificial intelligence models are trustworthy and responsible. The company has announced a big upgrade to its Model ...
VentureBeat

Weights & Biases weaves new LLMOps capabilities for AI development and model monitoring

Weights & Biases' platform includes tools that help enable an AI/ML development lifecycle. At the end of April, the company added new tools to enable LLMOps, that is, workflow operations for ...
techtarget

Managing drift in AI models and data

AI models never remain static; they inevitably drift over time. This makes continuous output monitoring and model drift mitigation vital to any ongoing AI strategy. AI systems are developed using ...
techtarget

SageMaker Studio makes model building, monitoring easier

LAS VEGAS -- AWS launched a host of new tools and capabilities for Amazon SageMaker, AWS' cloud platform for creating and deploying machine learning models; drawing the most notice was Amazon ...