Systems | Development | Analytics | API | Testing

We understand how complex app distribution can be, especially when juggling multiple tools for mobile app testing and store releases. This is what prompted us to build Release Management back in 2022. Originally tightly coupled to Bitrise CI, we're excited to announce that Release Management can now be used with any CI tool. This means all mobile teams can use Release Management, and we're offering it with a generous free tier, regardless of their CI tool.

As the open-source maintainers of MLRun, we’re proud to announce the release of MLRun v1.7. MLRun is an open-source AI orchestration tool that accelerates the deployment of gen AI applications, with features such as LLM monitoring, fine-tuning, data management, guardrails and more. We provide ready-made scenarios that can be easily implemented by teams in organizations.

We strive to improve clarity and user-friendliness, and have thus focused our efforts on several features that align with this objective. Our new Sidebar Menu, the Release Tracking Feature and an increased comparison range for Releases and Markers as well as the possibility to show error messages in notifications all fall into this category. Summary.

For the ninth consecutive time, Gartner has named Google Cloud Apigee a Leader in the 2024 Gartner Magic Quadrant for API Management.

The report highlights platforms like Fivetran that are crucial for modernizing marketing data infrastructure in today’s fast-paced, budget-conscious market.

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.

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.

Predictive machine learning continues to be a cornerstone of data-driven decision-making. However, as organizations accumulate more data in a wide variety of forms, and as modeling techniques continue to advance, the tasks of a data scientist and ML engineer are becoming increasingly complex. Oftentimes, more effort is spent on managing infrastructure, jumping through package management hurdles, and dealing with scalability issues than on actual model development.