If you’re a data scientist or you work with machine learning (ML) models, you have tools to label data, technology environments to train models, and a fundamental understanding of MLops and modelops.

For AI to become mainstream, it will need to move beyond small scale experiments run by data scientists ad hoc. The complexity of technologies used for data-driven machine and deep learning means that ...

For most professional software developers, using application lifecycle management (ALM) is a given. Data scientists, many of whom do not have a software development background, often have not used ...

Machines can be trained to classify images and thus identify tumors in CT scans, mineral compositions in rocks, or pathologies in optical microscopy analyses. This artificial intelligence technique is ...

The amount of labor that goes into machine learning is pretty daunting. And despite the obstacles tackled by open source contributions, some of the most hyped machine learning frameworks merely skim ...

University of Idaho receives over $6M in DoD grants to advance machine learning research for PTSD diagnosis and military ...

Databricks today unveiled a new cloud-based machine learning offering that’s designed to give engineer everything they need to build, train, deploy, and manage ML models. The new offering is designed ...

Since 2021, Korean researchers have been providing a simple software development framework to users with relatively limited ...

SAN FRANCISCO--(BUSINESS WIRE)--Herophilus, a leading biotechnology company developing neurotherapeutics to cure complex brain diseases, today announced the publication of research that describes a ...