High-quality machine learning (ML) models depend on accurately labeled, high-quality training, validation, and test data. As ML and deep learning models are increasingly integrated into production environments, it's becoming more important than ever to have customizable, real-time data labeling pipelines that can continuously receive and process unlabeled data. For example, you may want to create a consumer-facing application that regularly collects and sends new data objects to a data labeling pipeline, which produces labels and builds a dataset for model training or retraining. This pipeline creates a positive feedback loop that leads to more accurate, sophisticated models. Amazon SageMaker Ground Truth streaming labeling jobs provide infrastructure and resources to create a continuously running labeling job that receives new data objects on demand and sends them to human workers to be labeled. You can chain multiple streaming labeling jobs together to create more intricate and refined data labeling pipelines. Use this blog post to learn how to set up and customize Ground Truth streaming labeling jobs.