Advanced Functionality
=================================

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Advanced Algorithms
-------------------------------------------

.. toctree::
   :maxdepth: 1

   pytorch_bring_your_own_gan/build_gan_with_pytorch
   pytorch_deploy_pretrained_bert_model/pytorch_deploy_pretrained_bert_model
   mxnet_mnist_byom/mxnet_mnist



Parallelism with Data Distribution
--------------------------------

.. toctree::
   :maxdepth: 1

   data_distribution_types/data_distribution_types


Multi-Model Endpoints
-------------------------

.. toctree::
   :maxdepth: 1

   multi_model_pytorch/pytorch_multi_model_endpoint
   multi_model_catboost/multi_model_catboost

Prebuilt Deep Learning Containers
--------------------------------

.. toctree::
   :maxdepth: 1

   autogluon-tabular-containers/AutoGluon_Tabular_SageMaker_Containers


SageMaker Pipeline
--------------------------------

.. toctree::
   :maxdepth: 1

   autogluon-sagemaker-pipeline/sagemaker-pipelines-project

 
Multi Container Endpoint
--------------------------------

.. toctree::
   :maxdepth: 1

   multi-container-endpoint/direct-invocation/multi-container-direct-invocation


Bring Your Own Algorithm Container
--------------------------------

.. toctree::
   :maxdepth: 1

   multi_model_bring_your_own/multi_model_endpoint_bring_your_own


Fine-tuning and deploying a BERTopic model on SageMaker with your own scripts and dataset, by extending existing PyTorch containers
--------------------------------

.. toctree::
   :maxdepth: 1

   pytorch_extend_container_train_deploy_bertopic/BERTtopic_extending_container


Bring Your Own Pipe-Mode Algorithm
-------------------------------------

.. toctree::
   :maxdepth: 1

   pipe_bring_your_own/pipe_bring_your_own


Bring Your Own Model
-------------------------------------

.. toctree::
   :maxdepth: 1

   scikit_learn_bring_your_own_model/scikit_learn_bring_your_own_model


Casual Inference Container
-------------------------------------

.. toctree::
   :maxdepth: 1

   causal-inference/causal-inference-container