Bringing machine learning (ML) models into production is often hindered by fragmented MLOps processes that are difficult to scale with the underlying data. Many enterprises stitch together a complex mix of various MLOps tools to build an end-to-end ML pipeline. The friction of having to set up and manage separate environments for features and models creates operational complexity that can be costly to maintain and difficult to use.

In the mobile-first era, high-quality apps are essential to keep users happy and engaged. At our software testing company, we specialize in the intricacies of mobile app testing. This blog outlines best practices to guarantee your apps provide a smooth and reliable user experience.

As a software testing professional, you know the importance of efficient and reliable testing processes. With Katalon's StudioAssist, you can streamline your testing workflow and ensure high-quality results. In this comprehensive guide, you'll learn how to utilize StudioAssist for mobile, API, and web testing through practical examples and step-by-step instructions. Whether you're a seasoned tester or just starting out, this guide will help you harness the full potential of StudioAssist.

Two of the main tenets of Zero Trust are encryption between services and managing the connections each service is allowed to use. Achieving this generally falls to running a service mesh in a Kubernetes cluster. Refactoring applications to run properly in Kubernetes takes time and considerable investment. For many organizations, running their applications on virtual machines will be a necessity for years to come. However, this doesn't mean security should fall behind.

Why should you prioritize an API design-first approach in your development strategy? This essential methodology promotes early and thorough API specification, fostering a blueprint that guides the entire development process. You can cut straight to improved efficiency, consistency, and quality in your API projects by embracing this approach from the start.

Today’s data-driven world requires an agile approach. Modern data teams are constantly under pressure to deliver innovative solutions faster than ever before. Fragmented tooling across data engineering, application development and AI/ML development creates a significant bottleneck, hindering the speed of value delivery required to stay competitive. Disparate tools create a complex landscape for developers and data teams, hindering efficient pipeline development and deployment.

In the dynamic world of software development, quality is paramount. Every line of code, every feature, every functionality needs to be meticulously evaluated to ensure a flawless user experience. This is where test cases come in – the unsung heroes of the development process.

Discovering and surfacing telemetry traditionally can be a tedious and challenging process, especially when it comes to pinpointing specific issues for debugging. However, as applications and pipelines grow in complexity, understanding what’s happening beneath the surface becomes increasingly crucial. A lack of visibility hinders the development and maintenance of high-quality applications and pipelines, ultimately impacting customer experience.

Having a dedicated branching strategy is key for teams wanting to speed up their workflows, meet tasks on a deadline, and reduce code errors. But with all the different branching strategies – including release branching, trunk-based branching, feature branching, and more – it can be difficult to determine which one best fits your team’s needs.

The medical device industry has recently seen a troubling increase in recalls, often due to software failures or software-to-hardware miscommunication. The number of medical devices with software is also increasing, considering the rise in IoMT (Internet of Medical Things). These issues highlight the urgent need for thorough and robust testing methodologies to ensure the reliability and safety of medical devices.