Estimators are a fundamental concept in statistics and machine learning, serving as functions or algorithms that provide estimates of population parameters based on sample data. They can be classified 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 are model-level abstractions that simplify 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, exception handling, and checkpointing, which enhances the robustness of model training. They also promote a clear separation between the data input pipeline and the model, facilitating experimentation with different datasets ².

If you have more specific questions about estimators or their applications, feel free to ask!