This article is intended to users relying on machine learning solutions offered by third party vendors. It applies to platforms, dashboards, traditional software, or even external pieces of code that are too time consuming to modify. One of the goals is to turn such systems into explainable AI.
Technical Debt describes what results when development teams take conscious actions to expedite the delivery of a piece of functionality or a project which later needs to be remediated via refactoring.
With over a thousand apps launched daily, the market competition in mobile app development is intense. Going mobile is no longer an option for a… Read More »Factors to Consider while Developing Mobile Apps
Over recent years, web scraping has become an incredibly popular practice, the rise of this field being largely attributed to the vast amounts of data that are produced and distributed every single day.
Anti-money laundering (AML) and know-your-customer (KYC) compliance might be transformed by artificial intelligence (AI). Artificial intelligence systems may also mine vast amounts of data through… Read More »How AI is Stopping Money Laundering
Ontology Hack – Make Use of Existing Enterprise Data Assets Instead of Starting from Scratch As an author of a (reasonably) popular book, I often… Read More »How to Get Started on an Ontology Without Really Trying
With today’s customers becoming more tech-savvy and sophisticated, the software businesses are becoming extremely competitive where quality is a critical factor for software projects/products and… Read More »Functional Testing in Agile Environment: All You Need to Know
A subset of performance testing, load testing is just the concept of testing a given software’s ability to withstand the load, i.e., concurrent users. It… Read More »Load Testing: Top Tools
