As part of the graduation of custom merge checks from open-beta to general availability (GA) that is planned for late-April 2024, we're making a range of changes to several areas of the core Bitbucket Cloud workflow. This includes changes to the core pull request review and authoring experience, as well as changes to how merge checks are configured within a repository. Different changes will be relevant to different audiences, so feel free to use the navigation shortcuts below.

Today, modern software organizations’ requirements for DevOps tooling has become more sophisticated and bespoke. We hear from many customers that they are building large, complex systems to augment and extend their DevOps workflows in ways that don't work well with the tools they're using. This is why Bitbucket Cloud is on a mission to become the world's most extensible cloud SCM and CI/CD product.

A key challenge of monitoring your CI/CD system is understanding how to optimize your workflows and create best practices that help you minimize pipeline slowdowns and better respond to CI issues. In addition to monitoring CI pipelines and their underlying infrastructure, your organization also needs to cultivate effective relationships between platform and development teams.

Ciara Carey reflects on her experience at the KubeCon event in Paris including her top 10 talks and current industry trends.

Last November, Dimitris and Giordano Ricci wrote a blog post about CI/CD observability that looked into ways to extract traces and metrics in order to get a better understanding of possible issues inside a CI/CD system. That post focused on getting data from a continuous integration (CI) system, and it really resonated with the community.

Machine learning operations (MLOps) has become the backbone of efficient artificial intelligence (AI) development. Blending ML with development and operations best practices, MLOps streamlines deploying ML models via continuous testing, updating, and monitoring. But as ML and AI use cases continue to expand, a need arises for specialized tools and best practices to handle the particular conditions of complex AI apps — like those using large language models (LLMs).

The wins just keep coming! Today we celebrate fresh evidence of the mark we're making in the industry.

The collaboration between development (Dev) and operations (Ops) teams is crucial for delivering high-quality software products and services efficiently. DevOps has emerged as a transformative approach that bridges the gap between these two traditionally siloed functions, fostering a culture of collaboration, automation, and continuous improvement.

DevOps teams play a crucial role in ensuring the continuous delivery of software applications. One of the key pillars of DevOps success is implementing effective monitoring and alerting strategies. In this blog post, we will explore the importance of monitoring and alerting in DevOps, discuss best practices, and provide insights into building a robust monitoring ecosystem.

In this article, you will get an easy-to-understand explanation of what an API (application programming interface) is and how you can use them to enhance your software applications or services.