title: Saving, loading, and predicting from a TensorFlow Estimator model (2.0)
body: <p>Is there a guide anywhere for serializing and restoring <code>Estimator</code> models in TF2? The documentation is very spotty, and much of it not updated to TF2. I've yet to see a clear ands complete example anywhere of an <code>Estimator</code> being saved, loaded from disk and used to predict from new inputs. </p>
  
  <p>TBH, I'm a bit baffled by how complicated this appears to be. Estimators are billed as simple, relatively high-level ways of fitting standard models, yet the process for using them in production seems very arcane. For example, when I load a model from disk via <code>tf.saved_model.load(export_path)</code> I get an <code>AutoTrackable</code> object:</p>
  
  <p><code>&lt;tensorflow.python.training.tracking.tracking.AutoTrackable at 0x7fc42e779f60&gt;</code></p>
  
  <p>Its not clear why I don't get my <code>Estimator</code> back. It looks like there used to be a useful-sounding function <code>tf.contrib.predictor.from_saved_model</code>, but since <code>contrib</code> is gone, it does not appear to be in play anymore (except, it appears, in TFLite).</p>
  
  <p>Any pointers would be very helpful. As you can see, I'm a bit lost.</p>