Stories by Paul Krill

C++ developers look to these libraries for help building Windows apps, mobile apps, machine learning and scientific apps, and more

Amazon has launched an open source toolkit to help developers incorporate artificial intelligence in their applications

Common Python API for cloud-based AutoML services would allow data scientists to train their data sets against multiple AutoML models

Open source framework replaces boilerplate code in complex apps with the ability to compose and override configurations

Future plans for the project that brings Swift to machine learning include C++ interoperability, improved automatic differentiation, and support for distributed training

IBM has released Python toolkits for identifying and mitigating against bias in training data and machine learning models

At the current pace of growth, Tiobe estimates Python could surpass Java and C in popularity in three to four years

Decision services based on reinforcement learning and other machine learning enhancements are coming to the Azure cloud

The alternative to Ansible promises less work for automation developers

Pyodide project uses Emscripten and WebAssembly to run Python and its data science libraries in any major browser

The renamed Google Cloud Services Platform uses Kubernetes to build and manage hybrid applications for on-premises or the cloud

Google Cloud Run lets you deploy stateless, HTTP-invocable containers to a managed compute service or to Google Kubernetes Engine

Its lighter use of templates could make it easier to debug C++ applications

Java, C, C++, VisualBasic.Net, Kotlin, Matlab, Rust, and TypeScript also gain while Ruby and F# slip

An expected light touch may mean developers won’t notice much difference in how the language evolves