Operations | Monitoring | ITSM | DevOps | Cloud

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.