From exploratory data analysis to automated machine learning, look to these techniques to get data science projects moving and to build better models.
Stories by Martin Heller
While approaches and capabilities differ, all of these databases allow users to build machine learning models right where data resides.
Microsoft Azure combines a wide range of cognitive services and a solid platform for machine learning that supports automated ML, no-code/low-code ML, and Python-based notebooks.
Google Cloud AI and Machine Learning Platform is missing some pieces, and much is still in beta, but its scope and quality are second to none.
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.
Machine learning lifecycle management systems rank and track your experiments over time, and sometimes integrate with deployment and monitoring
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.
Facial recognition is becoming more accurate, but some systems exhibit racial bias and some uses of the technology are controversial.
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 3D views to managed provisioning processes, there’s a lot for Android app developers to love about Lollipop
From full-blown IDEs to essential resource utilities, these Android apps bring powerful programming features to phones and tablets