Quick, do you know what code is currently deployed to production? How about staging? How far apart are these environments? Sleuth tracks your deployments, and today, Sleuth is launching first-class environments support. I'm going to show you how it works, steps to take to migrate your existing projects, and where we plan to take it in the future.
Quick, do you know what code is currently deployed to production? How about staging? How far apart are these environments? Sleuth tracks your deployments, and today, Sleuth is launching first-class environments support. I'm going to show you how it works, steps to take to migrate your existing projects, and where we plan to take it in the future.
In order to have the most successful deployment, there are a few best practices that software development teams should be mindful of. First, following a continuous delivery approach allows software development teams to release in shorter cycles, so that building, testing, and releasing can be done with greater speed and reduced error rates. As stated in an earlier post of ours, you’re doing continuous delivery when: Your software is deployable throughout its lifecycle.
In order to have the most successful deployment, there are a few best practices that software development teams should be mindful of. First, following a continuous delivery approach allows software development teams to release in shorter cycles, so that building, testing, and releasing can be done with greater speed and reduced error rates.
I’ve recently co-founded a startup, and after working so long in more mature enterprises with diverse toolchains, it was actually massively educational to return to first steps and figure out what our critical needs were, balanced with essentially zero budget. As we’ve grown and raised money, we’ve graduated in some areas to other tools, but I wanted to share what we felt were the absolute can’t-live-without-tools for a team building SaaS software.
Deploying a new feature to production is a momentous occasion. It's important to ensure that everything goes properly at this stage, as deployments tend to be error-prone when not handled correctly. To examine why this is and how you can avoid it, let's take a look at the different types of deployments available and where some of them fall short.
Deploying a new feature to production is a momentous occasion. It's important to ensure that everything goes properly at this stage, as deployments tend to be error-prone when not handled correctly. To examine why this is and how you can avoid it, let's take a look at the different types of deployments available and where some of them fall short.
Your team is practicing DevOps and you’re delivering some flavor of Continuous Delivery. You’re deploying anywhere from three times a week to twenty times a day. You are moving fast! At that speed, how do you know if you are moving things in the right direction? Hopefully, your team has defined some key SLIs that define your application’s health.
Machine Learning (and deep learning) applications are quickly gaining in popularity, but keeping the process agile by continuously improving it is getting more and more complex. There are many reasons for this, but primarily, behaviors are complex and difficult to anticipate, making them resistant to proper testing, harder to explain, and thus not easy to improve.
Building a healthy productive engineering culture is hard and just deploying frequently with continuous deployment is not enough. I'm going to show you how you can use a carrot, not a stick, approach to take continuous deployment to the next level by celebrating team values with Sleuth.
