Containerized microservices are essential to cloud migration and digital transformation plans. Do you have a clear picture of what containerized microservices are and why they’re important? In this guide, you'll learn about containers, microservices, and how they work together. First up is a bit of history that explains how the need for containers came to be. Here's the key things to know about containerized microservices.

Developer Experience (DevEx) is a general term that encompasses all of the interactions a developer has with their work environment. The tools that a developer uses, the processes they employ, and even the organizational culture, all play a role in establishing a great Developer Experience – or, unfortunately, a bad one.

If you're worried about the potential for heightened regulatory scrutiny in financial services, you're not alone. Business operations teams everywhere are focused on the end-to-end, client lifecycle management (CLM) process as they cope with ever-changing regulations governing how, when, and where client data can be stored and accessed. It's hard to stay compliant when customer data is spread across multiple operational silos.

The process of preparing and submitting labeling to regulatory authorities can be time-consuming and complex, requiring specialists’ input and careful coordination across organizations and functions to ensure the quality and safety of products going to market. This often causes delays that ripple across the supply chain, impacting profitability and patient care.

Phoenix and Elixir are designed at their core to build real-time, fault-tolerant applications. With its elegant syntax and the robustness of the Erlang VM, Elixir is an ideal candidate for tackling the challenges of distributed state management. This two-part series will guide Phoenix/Elixir developers through the intricacies of working with Phoenix in a distributed setup.

Combining Artificial Intelligence and human intelligence in testing becameessential for delivering high-quality products quickly and efficiently. AI excels at automating repetitive tasks, analyzing vast datasets, and improving test coverage. Humans, on the other hand, bring creativity, critical thinking, and the ability to handle complex scenarios that machines can’t easily navigate. Together, they form a powerful synergy that enhances speed and accuracy in testing but also brings challenges..

Information extraction (IE) finds its roots in the early development of natural language processing (NLP) and artificial intelligence (AI), when the focus was still on rule-based systems that relied on hand-crafted linguistic instructions to extract specific information from text. Over time, organizations shifted to techniques like deep learning and recurrent neural networks (RNN) to improve the accuracy of information extraction systems.

Part 1 of this two-part blog serves as the foundation, highlighting the critical role that quality engineering (QE) must play in addressing the business risks associated with the accelerating adoption of artificial intelligence or AI-powered software.

The success of a product hinges on more than innovation; it depends on flawless performance.

The software testing market size was valued at USD 51.8 billion in 2023 and is projected to grow at a CAGR (Compound Annual Growth Rate) of over 7% between 2024 and 2032.