AI has had a massive impact on every part of our lives, but mainly on how we consume large data sets easily. The observability world is based on collecting enormous amounts of data and consuming it by observing dashboards built on monitoring tools. Most of the o11y tasks like writing complex queries, creating dashboards & defining alerts, have been done much in the same way for the last decade & AI models are well-positioned to disrupt this modus operandi.

Backstage is gaining wide adoption for platform engineering teams looking to build internal development platforms. After implementing Backstage, data has shown an improvement in 2X of code changes, and a decrease of 17% in cycle time making a huge impact on the business delivery pipeline for organizations. Backstage with its rich plugin ecosystem, makes it possible to get full troubleshooting and security coverage you need across your entire pipeline in a single dashboard and interface.

The need for automation is becoming more important day by day. The process of integrating written code with already working code and publishing new code to live environments is a very error-prone process. Performing static analysis, running tests, packaging, and versioning are tasks that require a lot of manual effort. It’s also a complex task to solve the problem of deploying the projects we develop to more than one environment, on more than one machine, without automation.