Menial tasks rob workers of time they could spend on more productive activities. Done right, RPA can banish bucketfuls of mindless chores.
Hosting CI/CD in the cloud can both speed up interactions between development pipelines and source code repositories and make life easier for developers.
From exploratory data analysis to automated machine learning, look to these techniques to get data science projects moving and to build better models.
Data wrangling and exploratory data analysis are the difference between a good data science model and garbage in, garbage out.
While approaches and capabilities differ, all of these databases allow users to build machine learning models right where data resides.
Deepfakes extend the idea of video compositing with deep learning to make someone appear to say or do something they didn’t really say or do.
Quantum computing has great promise to solve problems that are too hard for classical computers to solve but they are not yet practical.
Amazon’s quantum computing service is currently good for learning about quantum computing and developing NISQ-regime quantum algorithms.
12 capabilities every cloud machine learning platform should provide to support the complete machine learning lifecycle.
By hosting datasets, notebooks, and competitions, Kaggle helps data scientists discover how to build better machine learning models.
IoT is currently one of the most hyped concepts in the computing world. Cloud IoT platforms may even exceed IoT on the hype scale.
From the creator of Node.js, Deno is a secure runtime for JavaScript and TypeScript that addresses Node’s shortcomings.
