A secret to adopting machine learning that has nothing to do with the actual technology. Machine learning has the potential to transform your business. To automate processes, uncover new insights, make your products and services better, and customers happier. Integrating the capability into your organization requires operational transformation and lots (and lots) of experimentation. But, you know this already.

Ever since we started logging with Bugfender back in 2015, we’ve been working towards integration with Firebase, the app development platform created by Google. Firebase is famous for the breadth of its integration libraries and millions of people use the product around the world, drawn to its sleek UI and range of features. Our primary goal has been integration with Firebase Crashlytics Log, which underpins the entire platform.

At the end of 2018, Microsoft stated that Visual Studio 2019 is the last version with load testing features. A few months later they formally announced that Azure load testing is closing down on March 31st, 2020. This announcement came as a bomb to the community. But for those who follow this market, the news was not a complete surprise.

If software's been eating the world for the past twenty years, it's safe to say machine learning has been eating it for the past five. But what exactly is machine learning? Why should a web developer care? This article by Julie Kent answers these questions. I don't have kids yet, but when I do, I want them to learn two things: Whether or not you believe that the singularity is near, there's no denying that the world runs on data.

The theoretical bases for Machine Learning have existed for decades yet it wasn’t until the early 2000’s that the last AI winter came to an end. Since then, interest in and use of machine learning has exploded and its development has been largely democratized. Perhaps not so coincidentally, the same period saw the rise of Big Data, carrying with it increased distributed data storage and distributed computing capabilities made popular by the Hadoop ecosystem.

To understand what micro apps are on iOS, we first must look at what a “micro app” is. A micro app is a small, precise, highly specialized application. It is designed for a single use case. They can run independently, or you can combine several micro apps in pursuit of a more complex objective. Some example use cases include reading from or writing to an enterprise database, or sending alerts or notifications when a particular event occurs.

The introduction of AI into products and services – across all sectors – is creating new capabilities at a scale that software developers could never have dreamed of just a decade ago. But this development is not just about the tech.

If you’ve got an app that keeps going down for no apparent reason, don’t worry, this quick and easy guide will give you the tools to get it up and running smoothly. To help you better, first select what type of user you are and what problem you have: If you’re not from the tech world yourselves, you probably think us programmers get everything right all the time.

It has been almost three years since we first published our first comparison & benchmark articles that have become very popular, and we thought an update seemed overdue as some tools have changed a lot in the past couple of years. For this update, we decided to put everything into one huge article - making it more of a guide for those trying to choose a tool.