Martin Heller
Contributing Editor
Martin Heller is a contributing editor and reviewer for InfoWorld. Formerly a web and Windows programming consultant, he developed databases, software, and websites from his office in Andover, Massachusetts, from 1986 to 2010. More recently, he has served as VP of technology and education at Alpha Software and chairman and CEO at Tubifi. Disclosure: He also writes for Hewlett-Packard's TechBeacon marketing website.
Visual Studio Code vs. Sublime Text: How to choose
Do you want a maximum-flexible editor with just the right IDE features, or the fastest and most efficient editor you can find? You can have both
Visual Studio Code vs. Visual Studio: How to choose
Deciding between Visual Studio Code and Visual Studio may depend as much on your work style as on the language support and features you need. Here’s how to decide
Snowflake review: A data warehouse made better in the cloud
A fast, no-fuss data warehouse as a service, Snowflake scales dynamically to give you the performance you need exactly when you need it
Semi-supervised learning explained
Using a machine learning model’s own predictions on unlabeled data to add to the labeled data set sometimes improves accuracy, but not always
Deep learning frameworks: PyTorch vs. TensorFlow
If you actually need a deep learning model, PyTorch and TensorFlow are both good choices
Automated machine learning or AutoML explained
AutoML frameworks and services eliminate the need for skilled data scientists to build machine learning and deep learning models
Couchbase review: A smart NoSQL database
Flexible, distributed document database offers an easy query language, mobile synch, independently scalable services, and strong consistency within a cluster
The best NoSQL databases
Highly flexible and hugely scalable, NoSQL databases offer a range of data models and consistency options to suit your application
Unsupervised learning explained
Unsupervised learning is used mainly to discover patterns and detect outliers in data today, but could lead to general-purpose AI tomorrow
The best graph databases
These stellar databases combine horizontal scalability with highly efficient engines for storing and analyzing connected data
The best machine learning and deep learning libraries
TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models
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