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.

Machine learning (ML) has quickly become integral to many businesses. Its rapid adoption across almost every industry is because it is a force multiplier: new data can be learned from and understood as it arrives, while historical data can be revisited with new tools and practices.

Continuous Integration/Continuous Delivery (CI/CD) has become an essential part of modern software development, allowing teams to deliver high-quality code at a faster pace. Teams can either build or buy their CI/CD system. In this blog post, we will compare both options - exploring the advantages and disadvantages of each - and why CircleCI may be the better choice.