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CI CD

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

How to Combine Speed and Trust in Enterprise Software Development

Software development begins with code, which is then integrated, compiled, tested, and in the end distributed to users. This is often the secret sauce of innovation that organizations must protect to keep their competitive edge. With the software application development market growing at almost 30% per year and the average project taking just 4-6 months to complete – and trending downwards – the result is more software to develop and constant pressure to release it as fast as possible.

AI and the evolution of learning: Insights from Coding with Lewis

In this episode, Rob sits down with Lewis Menelaws from Coding with Lewis, a prominent social media influencer and content creator specializing in entertaining and empowering software developers. Together, they explore the evolving landscape of learning the craft, drawing comparisons between the present day and the learning experiences of 25 years ago.

Deploy a containerized .NET Core app to Azure Kubernetes Service (AKS)

Microsoft Azure provides an all-encompassing service that allows you to host Docker containers on the Azure Container Registry (ACR), deploy to a production-ready Kubernetes cluster via the Azure Kubernetes Service (AKS), and more. Using CircleCI, you can automatically deploy updates to your application, providing a safer and more efficient CI/CD process for managing your software. This article shows you how to automate deployments for a.Net application to Azure Kubernetes.

What is CI/CD observability, and how are we paving the way for more observable pipelines?

Observability isn’t just about watching for errors or monitoring for basic health signals. Instead, it goes deeper so you can understand the “why” behind the behaviors within your system. CI/CD observability plays a key part in that. It’s about gaining an in-depth view of the entire pipeline of your continuous integration and deployment systems — looking at every code check-in, every test, every build, and every deployment.

Deploy with Slack

Automate shipping code through Slack, or better yet, skip the manual steps altogether! This preview gives you a taste of what Slack approvals looks like and why you'd want to do it with Sleuth. Give Sleuth a try and see how we give teams actionable insights on how to improve with no-code automations to instantly ship improvements, and metrics to measure their impact — all in a way that both managers and developers love.

Ship code via Slack approvals

Automate shipping code through Slack, or better yet, skip the manual steps altogether! This video covers three steps to use Sleuth to automate code deployments using Slack. A bonus tip shows how to use Sleuth to automate the promotion of builds from staging to production, but in a safe manner with automatic health checks. Give Sleuth a try and see how we give teams actionable insights on how to improve with no-code automations to instantly ship improvements, and metrics to measure their impact — all in a way that both managers and developers love.

ML for software engineers ft. Gideon Mendels of Comet ML

In this episode, Rob explores the fascinating crossroads of machine learning and software engineering with Gideon Mendels, the co-founder and CEO of Comet ML. Gideon navigates the often ambiguous world of training ML models, focusing on building a common language between software engineers and data science teams. Gain valuable insights into fostering mutual understanding between these two disciplines and aligning the possibilities of ML with organizational needs in this thought-provoking episode.

Goodbye, GitOps: Getting to green in an AI-powered world

The cognitive bias known as the streetlight effect describes our desire as humans to look for clues where it’s easiest to search, regardless of whether that’s where the answers are. For decades in the software industry, we’ve focused on testing our applications under the reassuring streetlight of GitOps. It made sense in theory: wait for changes to the codebase made by engineers, then trigger a re-test of your code. If your tests pass, you’re good to go.