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.

Artificial intelligence (AI) is transforming our world, but within this broad domain, two distinct technologies often confuse people: machine learning (ML) and generative AI. While both are ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

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 ...

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 ...

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 ...

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 ...

Here are 8 BPO players that are going heavy on machine learning: Genpact, founded in 1997, uses machine learning in finance, supply chain tasks, and customer-cycle management for large enterprises.