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.
How to choose a low-code development platform
Low-code and no-code development platforms allow developers and in some cases non-technical users to produce apps faster and at lower cost than traditional programming. Here’s how to select the right low-code or no-code tool for the...
How to choose a cloud-based CI/CD platform
Hosting CI/CD in the cloud can both speed up interactions between development pipelines and source code repositories and make life easier for developers.
Dataiku review: Data science fit for the enterprise
Dataiku’s end-to-end machine learning platform combines visual tools, notebooks, and code to address the needs of data scientists, data engineers, business analysts, and AI consumers.
8 databases supporting in-database machine learning
While approaches and capabilities differ, all of these databases allow you to build machine learning models right where your data resides.
Oracle Database 21c review: The old RDBMS is new again
Major upgrade to the ever-evolving Oracle Database brings JavaScript support, graph optimizations, in-memory enhancements, and dramatic improvements to JSON operations and in-database machine learning.
Ahana Cloud for Presto review: Fast SQL queries against data lakes
Ahana Cloud for Presto turns a data lake on Amazon S3 into what is effectively a data warehouse, without moving any data. SQL queries run quickly even when joining multiple heterogeneous data sources.
8 ways to jump-start your machine learning
From exploratory data analysis to automated machine learning, look to these techniques to get your data science project moving — and to build better models.
Data wrangling and exploratory data analysis explained
Data rarely comes in usable form. Data wrangling and exploratory data analysis are the difference between a good data science model and garbage in, garbage out.
BlazingSQL review: Fast ETL for GPU-based data science
BlazingSQL builds on RAPIDS to distribute SQL query execution across GPU clusters, delivering the ETL for an all-GPU data science workflow.
How to choose a cloud serverless platform
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.
Review: AWS AI and Machine Learning stacks up, and up
Amazon Web Services provides an impressively broad and deep set of machine learning and AI services, rivaling Google Cloud and Microsoft Azure.
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