Stories by Martin Heller
Data wrangling and exploratory data analysis are the difference between a good data science model and garbage in, garbage out.
From AWS Lambda and Azure Functions to Knative and OpenFaaS, we have at least a dozen functions-as-a-service platforms to choose from. Here’s how to navigate the options.
Amazon Web Services provides an impressively broad and deep set of machine learning and AI services, rivalling Google Cloud and Microsoft Azure.
From exploratory data analysis to automated machine learning, look to these techniques to get data science projects moving and to build better models.
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.