This article looks at 13 open source projects that are remaking the world of AI and machine learning. Some are elaborate software packages that support new algorithms. Others are more subtly ...

Applications; Big Data and Analytics; IT Management; Why Machine Learning Projects Fail – and How to Make Sure They Don’t. The first step to a successful ML project is to understand that these ...

Tweaking machine learning algorithms and models won't always be for experts only, thanks to these cutting-edge projects Topics Spotlight: New Thinking about Cloud Computing ...

Sai Krishna's work in text mining has earned him well-deserved recognition within his organization. His development of an RShiny-based machine learning workbench was a game-changer, leading to ...

It is a natural goal of any organization to maximize the return on investment of their machine learning (ML) projects. To do this, an organization must be aware of a number of pitfalls that can ...

The results so far suggest that investing in AI puts multiple rewards within reach. Machine learning has the potential to dramatically speed up the analysis of big data across different domains, ...

High-throughput robotic collection, imaging, and machine learning analysis of salt patterns: composition and concentration from dried droplet photos. Digital Discovery , 2025; DOI: 10.1039/D4DD00333K ...

Swift, notes Burkov, has static typing and low availability of machine learning libraries/data analysis. Other options suggested by contributors in the same thread are Golang, Julia, and Rust.