Estimators are a high-level abstraction in TensorFlow that simplify the process of building machine learning models. They provide a framework for training, evaluating, and making predictions without the need to manage the underlying computational graph directly. Estimators can run on various hardware configurations, including CPUs, GPUs, and TPUs, and support distributed training across multiple servers. They also offer built-in functionalities for data handling, error management, and model checkpointing. While pre-made estimators are available for common tasks, users can also create custom estimators to meet specific needs. However, it’s important to note that estimators are not recommended for new code due to compatibility issues with TensorFlow 2.x ¹²⁴.