Estimators are fundamental components in statistics and machine learning, serving as rules or formulas used to infer the value of a population parameter based on sample data. They can be categorized into different types, such as point estimators and interval estimators, depending on the nature of the estimation.

In the context of machine learning, particularly with TensorFlow, estimators provide a model-level abstraction that simplifies the process of building and training models. The tf.estimator API allows for seamless execution of models on various hardware configurations, including CPUs, GPUs, and TPUs, without requiring significant changes to the model code. This flexibility is beneficial for both local and distributed training environments ².

Moreover, estimators help manage the training process by controlling data loading, handling exceptions, creating checkpoint files, and saving summaries for visualization in TensorBoard. This structured approach allows developers to focus on model design rather than the underlying complexities of the training loop ².

Overall, estimators are essential for efficient statistical inference and model training in machine learning frameworks.