Containers and microservices have revolutionized the way applications are deployed on the cloud. Since its launch in 2014, Kubernetes has become a de-facto standard as a container orchestration tool. With traditional approaches of deploying applications in production, developers often release updates or new features all at once, which can lead to issues if there are bugs or other issues that weren’t caught during testing.

Feature flags (also known as feature toggles or switches) are conditional statements in code that determine whether a feature or functionality is visible and accessible to users of an application or service. They offer programmers a powerful tool for managing feature releases. Their capabilities are indispensable in software development, where agility and continuous, automated delivery are paramount.

As organizations continue to embrace CI/CD and DevOps in their quest for shorter, more reliable delivery cycles, the choice of programming languages becomes even more critical. The language used to build your applications can affect everything from developer happiness and productivity to your organization’s performance on the four key software delivery metrics.

While there are an increasing number of off-the-shelf machine learning (ML) solutions that promise to adapt to your specific requirements, organizations that are serious about investing in ML for the long term are building their own workflows tailored exactly to their data and the outcomes they expect. To make full use of this investment, ML models must be kept up to date and working from the freshest available data.