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CI CD

The latest News and Information on Continuous Integration and Development, and related technologies.

Ship faster by integrating AI into your Bitbucket workflow

AI tools have taken the world by storm. In April, we announced Atlassian Intelligence to bring the power of AI into our tools. Leveraging AI through internal models and our collaboration with OpenAI, Atlassian Intelligence will be built into the Atlassian suite of tools, including Bitbucket Cloud.

Understanding and Optimizing CI/CD Pipelines

Building, testing and deploying software is a time-consuming process that many organizations aim to minimize by automating repeatable work wherever possible. To do so, many organizations are utilizing a continuous integration, continuous delivery (CI/CD) philosophy in combination with cloud native tools like Kubernetes to develop and deploy software at scale.

Here's what it feels like to deploy every day

Here's what it feels like to deploy every day. With Sleuth, Gigpro's software engineering team went from one deploy every two weeks to once a day. That made releases less stressful and helped improve team culture. Give Sleuth a try and see how we empower software teams to build faster by making engineering efficiency easy to improve and measurable — in a way that both managers and developers love.

GitOps the Planet #16: Using SLOs to Improve Software Delivery

Kit Merker is the one of the original product managers for Kubernetes and now Chief Growth Officer at Nobl9 where they're delivering a new open standard called OpenSlo. SLOs, or service-level-objectives, provide a framework for understanding performance targets and making judgements about software changes and how they impact uptime. But it's not just a standard, it's also code. Come find out about it with Kit in this GitOps the Planet!

CD for machine learning: Deploy, monitor, retrain

While there are an increasing number of off-the-shelf machine learning (ML) solutions that promise to adapt to your specific requirements, organizations that are serious about investing in ML for the long term are building their own workflows tailored exactly to their data and the outcomes they expect. To make full use of this investment, ML models must be kept up to date and working from the freshest available data.