Update doc/overview.rst

Co-Authored-By: Marcio Vinicius dos Santos <[email protected]>

Author: eslesar-aws

doc/overview.rst

@@ -30,7 +30,7 @@ After you train a model, you can save it, and then serve the model as an endpoin
 Prepare a Training script
 =========================
 
-Your training script must be a Python 2.7 or 3.5 compatible source file.
+Your training script must be a Python 2.7 or 3.6 compatible source file.
 
 The training script is very similar to a training script you might run outside of SageMaker, but you can access useful properties about the training environment through various environment variables, including the following:
 
@@ -956,4 +956,4 @@ Example code using the TensorFlow predictor:
     from sagemaker.tensorflow import TensorFlowPredictor
 
     predictor = TensorFlowPredictor('myexistingendpoint')
-    result = predictor.predict(['my request body'])
+    result = predictor.predict(['my request body'])