In this series of posts, we'll cover how Gretel's synthetic data platform helps you overcome challenges across the data-centric machine learning life cycle to help you successfully build, deploy, maintain, and realize value from your AI projects. The life cycle outlined below is a common framework or workflow process for building machine learning and AI solutions. It's focused on streamlining the stages necessary to develop machine learning models, deploy them to production, and maintain and monitor them. These steps are a collaborative process, often involving data scientists and DevOps engineers. The process below was inspired by the value chains created by The Sequence, Databricks, Google Cloud, and Microsoft.