In recent years, machine learning operations (MLOps) have become the standard practice for developing, deploying, and managing machine learning models. MLOps standardizes processes and workflows for faster, scalable, and risk-free model deployment, centralizing model management, automating CI/CD for deployment, providing continuous monitoring, and ensuring governance and release best practices.

We’re excited to unveil a sneak peek of the upcoming NeoLoad 2024.3 release, which will be generally available this November. One of the most anticipated features in this release is remote terminal emulation (RTE) support — a game-changer for teams responsible for the performance of mainframe and other legacy systems.

Many vendors have started calling their platforms “low-code” or “no-code.” Competitors often pit the terms against each other. But what do low-code and no-code really mean? And what's the difference between the two? Both platforms offer improvements over traditional high-code approaches. The biggest differences are the target user groups.

Keep users informed and engaged with Ably's expanded push notification support. We're thrilled to announce that Ably Pub/Sub now supports web push notifications, in addition to our existing support for iOS and Android devices. With just a few lines of code, developers can easily integrate web push into their applications, providing a seamless and unified experience across all major platforms.

Whether you agree with Elon's prediction or not, it's hard to ignore AI's far-ranging impact, especially on how we approach work. Over the last two years, we have seen AI progress rapidly, leaving many of us wondering, "Will AI replace my job?" It's a question that software engineers have also been grappling with. As ironic as it may seem, the people writing the code driving the technology revolution face the same uncertainty about whether AI might replace them in the future.

Every software application undergoes rigorous functional and non-functional testing to meet business requirements. When we talk about delivering top-tier software, performance is key. How fast does it run? How stable is it under pressure? Can it scale with growing demand? Does it remain reliable when pushed to its limits? These questions are at the core of performance testing, and that’s where benchmark testing steps into the spotlight.

In this blog post, we'll talk about how to implement API product tiering, a powerful way to customize access, control usage, and monetize APIs. This model can help you scale efficiently while maintaining a smooth user experience. Read on to learn more! We'll be talking about how to implement API product tiering with Kong Konnect, an API lifecycle management platform designed from the ground up for the cloud-native era and delivered as a service.

Adobe launched Adobe Experience Platform Federated Audience Composition, now generally available on Snowflake, allowing organizations to unlock seamless interoperability for marketers by integrating Snowflake's AI Data Cloud with Adobe Real-Time Customer Data Platform (CDP) and Adobe Journey Optimizer.

Companies are under pressure to deliver faster innovation, enabled by cloud-based data analytics and AI. In order to deliver faster business value, data teams are looking to achieve speed and scale through data and AI pipeline performance and efficiency. A recent MIT Technology Review Insights report finds that 72% of technology leaders agree that data challenges are the most likely factor to jeopardize AI/ML goals.

Modern data analytics platforms like Databricks have become indispensable for many companies. However Databricks costs can become unpredictable. This article gives four steps to help companies like yours predict Databricks costs and save your company time and money.