Human-in-the-loop machine learning takes advantage of human feedback to eliminate errors in training data and improve the accuracy of models.
Stories by Martin Heller
The public cloud is teeming with the latest and greatest development, devops, and AI tools for building better and smarter applications faster.
Copilot technical preview doesn’t always generate good, correct, or even running code, but it’s still somewhat useful. Future versions could be real time-savers.
Enterprise data warehouses are comprehensive structured data stores designed for analysis. They often serve as the data sources for BI systems and machine learning.
A good low-code development platform can help developers build apps faster at lower cost. A no-code platform allows non-programmers to contribute to development.
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.