[DOCS] Add ml batch inference tutorials (#2057)

Co-authored-by: Jay Chia <[email protected]@users.noreply.github.com>

daft/table/table_io.py

@@ -414,8 +414,6 @@ def write_tabular(
     partition_null_fallback: str = "__HIVE_DEFAULT_PARTITION__",
 ) -> MicroPartition:
 
-    pass
-
     [resolved_path], fs = _resolve_paths_and_filesystem(path, io_config=io_config)
     if isinstance(path, pathlib.Path):
         path_str = str(path)

docs/source/user_guide/tutorials.rst

@@ -31,6 +31,18 @@ Generate images from text prompts using a deep learning model (Mini DALL-E) and
 `Run this tutorial on Google Colab <https://colab.research.google.com/github/Eventual-Inc/Daft/blob/main/tutorials/text_to_image/text_to_image_generation.ipynb>`__
 
 
+.. These can't be run because DeltaLake can't be accessed in anonymous mode from Google Colab
+.. ML model batch inference/training on a Data Catalog
+.. ---------------------------------------------------
+
+.. Run ML models or train them on data in your data catalog (e.g. Apache Iceberg, DeltaLake or Hudi)
+
+.. 1. `Local batch inference <https://colab.research.google.com/github/Eventual-Inc/Daft/blob/main/tutorials/delta_lake/1-local-image-batch-inference.ipynb>`__
+.. 1. `Distributed batch inference <https://colab.research.google.com/github/Eventual-Inc/Daft/blob/main/tutorials/delta_lake/2-distributed-batch-inferece.ipynb>`__
+.. 1. `Single-node Pytorch model training <https://colab.research.google.com/github/Eventual-Inc/Daft/blob/main/tutorials/delta_lake/3-pytorch-ray-single-node-training.ipynb>`__
+
+
+
 .. Other ideas:
 .. Scaling up in the cloud with Ray **[Coming Soon]**
 .. Building a HTTP service **[Coming Soon]**