Operations | Monitoring | ITSM | DevOps | Cloud

We're excited to announce two major additions to our website that are set to make your experience using Cycle smoother and more effective: the completely revamped Documentation section, and the new Learn Center.

As a straightforward definition, AI inference is the process of applying a pre-trained machine learning model to new, unseen data in order to generate predictions, classifications, or decisions. Unlike the training phase, where the model learns from a dataset, inference involves utilizing the learned patterns to analyze and interpret new inputs.

Docker is an open-source container service provider, designed to help developers build, run, and share container applications. Users building and running these container applications need to conduct effective debugging and monitoring practices and for this, they have turned to Docker logging. To understand the importance of this, the latest edition of our how-to guide series surrounds Docker.

As developers, one of the most important things we can consider when designing and building applications is the ability to know if our application is running in an ideal operating condition, or said another way: the ability to know whether or not your application is healthy. This is particularly important when deploying your application to Kubernetes. Kubernetes has the concept of container probes that, when used, can help ensure the health and availability of your application.

Transactions-per-Second (TPS) is a valuable metric for evaluating system performance and is particularly relevant for engineers overseeing Kubernetes environments.TPS, alongside average response time, provides critical insights into system performance during load testing. This post covers two approaches to calculating TPS; a manual approach applicable in all environments, and an automatic Kubernetes-specific solution using production traffic replication.

At some point, your development team may be considering implementing load testing (also known as stress testing) as part of your software testing process. Load testing validates that your web app is able to withstand a large number of simultaneous users, decreasing the chance that any traffic spikes will bring down your services once deployed. These stress tests can be highly granular, giving you the opportunity to test run virtually unlimited strategies before they are set into the wild.

Kubernetes is known for its modularity, and its integration with cloud environments. Throughout its history, Kubernetes provided in-tree cloud provider integrations with most providers, allowing us to create cloud-related resources via API calls without requiring us to jump through hoops to deploy a cluster that utilizes the power of underlying networking infrastructure. However, this behavior will change with the release of Kubernetes v1.31, and right now is the best time to plan for it.