I believe an approach to machine learning deployment that’s based on an industry standard, language-agnostic, and able to represent a broad range of algorithms is the clear path forward.

Designed to support the entire machine learning lifecycle -- from data ingestion and model training to deployment and monitoring -- Azure ML is empowering developers to integrate predictive ...

The open-source platform from LF Networking could be a game changer for AI networking applications. Essedum is a specialized ...

Learn how to organize and structure your machine learning projects for real-world deployment. From directory layout to model ...

Gynecological cancers, including breast, ovarian, and cervical malignancies, account for a significant global health burden among women. The review outlines how a spectrum of machine learning (ML) ...

Drawing on real-world use cases and my own experience as a leader in this space, I'll describe how cloud-native technologies ...

Prospective silent deployment and evaluation of an intelligent machine learning model for prediction of emergency department visits during cancer treatment.

Citing vulnerabilities and biased outputs in the program, multiple advocacy organizations and nonprofits signed a letter to the Office of Management and Budget asking it to bar Elon Musk’s Grok from ...

The life and health insurance industry landscape across the globe is now confronting mounting challenges such as increasingly ...