Machine learning operations (MLOps) is a practice that focuses on the operationalization of machine learning models. It involves automating and streamlining the lifecycle of ML models, from development and training to deployment and monitoring. Much like data operations (DataOps), MLOps aims to improve the speed and accuracy of the data you’re accessing and analyzing.

Imagine you need to explain the key differences between Test Plans and Test Strategies at a software developer’s meeting. You know there’s a distinction, but the details blur together. Don’t worry; you’re not alone. This post which includes some test plan examples, is designed to clear up the confusion surrounding these crucial documents, essential for understanding the framework of successful software testing.

When AI first started to gain widespread adoption, it sparked a wave of fear. While much of that fear was overblown, we still need to remain cautious about any new technological innovation. Given AI’s potential to drive change on a massive scale, applying ethical principles to AI is not just important, but urgent. Every company must prioritize responsible AI—not only as an ethical responsibility but as a practical, strategic choice.

From regular buttons, to tab bar elements, even to our SwiftUI app’s icon, Images are a very important aspect of every mobile device application. In this article we’ll look at how to show images in SwiftUI, and give you several tips, with examples, on how to use those Images in your own apps. In this article any image used was sourced from Pexels, a website that hosts a database of free-to-use and royalty-free images.

At API Summit, Kong announced the upcoming beta release of a brand new gateway deployment method in Kong Konnect: Serverless Gateways. Designed for speed, simplicity of use, and affordability, it’s the quickest way to deploy a Kong Gateway. Today, we're pumped to announce that all Konnect Plus users can deploy a fully managed Serverless Gateway!

Need groceries? Just order online. Want printouts on priority? Upload it online and get it delivered to your doorstep in no time. Looking for a cook? Well, there’s an app for that too! So, why should the pharmacy industry be left behind? That’s why the demand for pharmacy app development came into the picture. Reason?

Let’s be honest – AI can seem like a bit of a mystery, and with this mystery comes myths and misconceptions. Is it actually that good? Can it handle varying document structures? Can it integrate with my existing systems? Because of this mystery, many companies have yet to take the leap and incorporate AI into their data processes. Today, we’re going to play MythBusters, separate fact from fiction, and show how you can use AI document processing to maximize efficiency and save costs.

Consider the following: You go to the Apple Store to pick up the latest iPhone. You get home and turn it on, only to find that the screen is defective, the buttons aren’t working, and every one of the built-in apps is glitching. Thanks to QA tools, this is an extremely unlikely scenario. Before the iPhone reaches your hands, both its hardware and software have been tested repeatedly by a Quality Assurance (QA) team.