In today’s rapidly changing world, software products need to be upgraded frequently and quickly to bring value to customers and users. Software features are continuously developed, tested, deployed, and operated in the production environment. New features are not only developed and tested successfully, but they also deployed and operated without much chaos and disruption.
Continuous Integration and continuous delivery (CI/CD) is a complex part of any development cycle. It involves continuously integrating code into a shared repository to keep code progression amongst a team of developers running smooth and steady. This helps prevent merging errors, duplicated efforts and promoting collaboration to create a better product. That code is then thoroughly and continuously tested to keep problems from arising.
New York City — June 20, 2019 — API Fortress announces the release of Forge, a lightweight downloadable test editor that increases flexibility in how you choose to write API tests. With Forge, a user can write a test with API Fortress on their own computer, outside of the platform. In addition, API Fortress is releasing apif-local, an application that contains the core of the API Fortress platform.
There used to be a distinct, technical separation between terms such as AI and machine learning (ML) – but only while these technologies remained largely theoretical. As soon as they became practical in the real world, and then commodifiable into products, the marketers stepped in. Widespread overuse of the terms AI/ML in marketing have managed to thoroughly confuse the meanings of these words.
If you happen to be running multiple clusters, each with a large number of services, you’ll find that it’s rather impractical to use static alerts, such as “number of pods < X” or “ingress requests > Y”, or to simply measure the number of HTTP errors. Values fluctuate for every region, data center, cluster, etc. It’s difficult to manually adjust alerts and, when not done properly, you either get way too many false-positives or you could miss a key event.
Ready to unlock the power of machine learning to accelerate diagnostics and activate AI-powered root-cause analysis? Here's how to get started.
As machine learning continues to address common use cases it is crucial to consider what it takes to operationalize your data into a practical, maintainable solution. This is particularly important in order to predict customer behavior more accurately, make more relevant product recommendations, personalize a treatment, or improve the accuracy of research.
