Pre-made Estimators enable you to work at a much higher conceptual level than the base TensorFlow APIs. You no longer have to worry about creating the computational graph or sessions since Estimators handle all the "plumbing" for you. Furthermore, pre-made Estimators let you experiment with different model architectures by making only minimal code changes. tf.estimator.DNNClassifier, for example, is a pre-made Estimator class that trains classification models based on dense, feed-forward neural networks.
A TensorFlow program relying on a pre-made Estimator typically consists of the following four steps :
1. Write an input functions
2. Define the feature columns
3. Instantiate the relevant pre-made Estimator.
4. Call a training, evaluation, or inference method
