Fix links to other TensorFlow docs

Author: smokestacklightnin

docs/get_started.ipynb

@@ -386,7 +386,7 @@
         "## Data Formats and Schema\n",
         "\n",
         "TFT Beam implementation accepts two different input data formats. The\n",
-        "\"instance dict\" format (as seen in the example above and [simple.ipynb](https://www.tensorflow.org/tfx/tutorials/transform/simple) \u0026 [simple_example.py](https://github.com/tensorflow/transform/blob/master/examples/simple_example.py))\n",
+        "\"instance dict\" format (as seen in the example above and [simple.ipynb](https://tensorflow.github.io/tfx/tutorials/transform/simple) \u0026 [simple_example.py](https://github.com/tensorflow/transform/blob/master/examples/simple_example.py))\n",
         "is an intuitive format and is suitable for small datasets while the TFXIO\n",
         "([Apache Arrow](https://arrow.apache.org)) format provides improved performance\n",
         "and is suitble for large datasets.\n",
@@ -562,7 +562,7 @@
         "## Compatibility with TensorFlow\n",
         "\n",
         "`tf.Transform` provides support for exporting the `transform_fn` as\n",
-        "a SavedModel, see the [simple tutorial](https://www.tensorflow.org/tfx/tutorials/transform/simple) for an example. The default behavior before the `0.30` release\n",
+        "a SavedModel, see the [simple tutorial](https://tensorflow.github.io/tfx/tutorials/transform/simple) for an example. The default behavior before the `0.30` release\n",
         "exported a TF 1.x SavedModel. Starting with the `0.30` release, the default\n",
         "behavior is to export a TF 2.x SavedModel unless TF 2.x behaviors are explicitly\n",
         "disabled (by calling `tf.compat.v1.disable_v2_behavior()`).\n",
@@ -753,7 +753,7 @@
         "    from the type and shape information. One can get the Schema either by\n",
         "    using a helper function we provide to translate from TF parsing specs\n",
         "    (shown in this example), or by running\n",
-        "    [TensorFlow Data Validation](https://www.tensorflow.org/tfx/tutorials/data_validation/tfdv_basic).\n",
+        "    [TensorFlow Data Validation](https://tensorflow.github.io/tfx/tutorials/data_validation/tfdv_basic).\n",
         "  - a list of column names, in the order they appear in the CSV file. Note\n",
         "    that those names must match the feature names in the Schema."
       ]
@@ -1047,7 +1047,7 @@
         "id": "OllPVQJl2dRx"
       },
       "source": [
-        "To see how to use these artifacts refer to the [Advanced preprocessing tutorial](https://www.tensorflow.org/tfx/tutorials/transform/census)."
+        "To see how to use these artifacts refer to the [Advanced preprocessing tutorial](https://tensorflow.github.io/tfx/tutorials/transform/census)."
       ]
     }
   ],

docs/tft_bestpractices.md

@@ -4,7 +4,7 @@ This document is the first in a two-part series that explores the topic of data
 
 This document assumes that you're familiar with [BigQuery](https://cloud.google.com/bigquery/docs), [Dataflow](https://cloud.google.com/dataflow/docs), [Vertex AI](https://cloud.google.com/vertex-ai/docs/start/introduction-unified-platform), and the TensorFlow [Keras](https://www.tensorflow.org/guide/keras/overview) API.
 
-The second document, [Data preprocessing for ML with Google Cloud](https://www.tensorflow.org/tfx/tutorials/transform/data_preprocessing_with_cloud), provides a step-by-step tutorial for how to implement a `tf.Transform` pipeline.
+The second document, [Data preprocessing for ML with Google Cloud](https://tensorflow.github.io/tfx/tutorials/transform/data_preprocessing_with_cloud), provides a step-by-step tutorial for how to implement a `tf.Transform` pipeline.
 
 ## Introduction
 
@@ -261,7 +261,7 @@ The following table summarizes the data preprocessing options that this document
 
 ## What's next
 
--   To implement a `tf.Transform` pipeline and run it using Dataflow, read part two of this series, [Data preprocessing for ML using TensorFlow Transform](https://www.tensorflow.org/tfx/tutorials/transform/data_preprocessing_with_cloud).
+-   To implement a `tf.Transform` pipeline and run it using Dataflow, read part two of this series, [Data preprocessing for ML using TensorFlow Transform](https://tensorflow.github.io/tfx/tutorials/transform/data_preprocessing_with_cloud).
 -   Take the Coursera specialization on ML with [TensorFlow on Google Cloud](https://www.coursera.org/specializations/machine-learning-tensorflow-gcp).
 -   Learn about best practices for ML engineering in [Rules of ML](https://developers.google.com/machine-learning/guides/rules-of-ml/).
--   For more reference architectures, diagrams, and best practices, explore the [TFX Cloud Solutions](https://www.tensorflow.org/tfx/guide/solutions){track-type="tutorial" track-name="textLink" track-metadata-position="nextSteps"}.
+-   For more reference architectures, diagrams, and best practices, explore the [TFX Cloud Solutions](https://tensorflow.github.io/tfx/guide/solutions){track-type="tutorial" track-name="textLink" track-metadata-position="nextSteps"}.