Hey there. Welcome to the fifth and final installment in our series on successfully deploying an app to AWS with the least effort. This week, we'll discuss things you need to worry about now that you've released, plus optimizing your app. Ok, so you've released your app. Congrats! However, we both know your work doesn't end here. You should continue improving your app, both on the end-user side and the back end.
Today we launched the Rancher Academy, our new free training portal. The first course is Certified Rancher Operator: Level 1, and in it you’ll learn exactly how to deploy Rancher and use it to deploy and manage Kubernetes clusters. This professional certification program is designed to help Kubernetes practitioners demonstrate their knowledge and competence with Kubernetes and Rancher – and to advance in their careers.
For teams that deal with machine learning (ML), there comes a point in time where training a model on a single machine becomes untenable. This is often followed by the sudden realization that there is more to machine learning than simply model training. There are a myriad of activities that have to happen before, during and after model training. This is especially true for teams that want to productionize their ML models.
Buildah is a tool for building OCI-compatible images through a lower-level coreutils interface. Similar to Podman, Buildah doesn't depend on a daemon such as Docker or CRI-O, and it doesn't require root privileges. Buildah provides a command-line tool that replicates all the commands found in a Dockerfile. This allows you to issue Buildah commands from a scripting language such as Bash.
The code we develop ends up being packaged into artifacts that are consumed as dependencies during the development of other software components. We rely on an artifact repository manager, like JFrog Artifactory, to resolve the complex challenges that come with consuming and developing all of these artifacts. Artifactory serves as the foundation for managing binaries in so many organizations, both large and small, as part of an effective CI/CD pipeline.
DevOps can successfully remove the bottlenecks created by waterfall methodology by improving development productivity through encouraging collaboration,continuous feedback loops, and automating processes. Collaboration. Feedback. Automation. These are the main building blocks of DevOps operations. However, not all DevOps implementations are successful. Why? Building blocks are not enough. You need to take these building blocks and design a DevOps pipeline that suits your needs.
How do you know if the changes that you’re making to your microservices are having the desired impact to your end-users? What happens after a deploy? Did your app’s performance remain the same? Were any bugs introduced? Do you need to roll back? With Codefresh and Lightstep, you’re able to more quickly, more accurately, and more easily ensure that any commit and build results in a better experience for your end-users.
Passing an audit is hard — whether you’re in retail, healthcare, finance, or honestly, any industry that values security. Most organizations these days are faced with regulatory standards that must be enforced, which bring both technical and business challenges that are difficult to overcome.
We’re excited to announce our support for GitHub Actions with our new Rollbar Deploy GitHub Action. Teams using GitHub Actions for building their CI/CD pipeline can now easily send deploy information to Rollbar and respond to any new errors quickly.
A major goal of DevOps is task and process automation. While total automation is possible to a large extent, DevOps engineers can’t just run a process and then leave it. They need to quickly react in the event of downtime and incidents. Usually, it’s important to react as fast as possible because downtime for even a few minutes can cost from $2,300 to $9,000 according to the Ponemon Institute in 2016 and a 14-hour outrage cost Facebook an estimated $90 million in March 2019.
