SageMaker Algorithms with Pre-Trained Model Examples by Problem Type

The SageMaker Python SDK provides built-in algorithms with pre-trained models from popular open source model hubs, such as TensorFlow Hub, PyTorch Hub, and Hugging Face. Customers can deploy these pre-trained models as-is, or first fine-tune them on a custom dataset and then deploy to a SageMaker endpoint for inference.

This section provides example notebooks for different ML problem types supported by SageMaker built-in algorithms. Please visit Use Built-in Algorithms with Pre-trained Models in SageMaker Python SDK for more documentation.

Example notebooks by problem type
Problem types
Supports
inference
with
pre-trained
models
Trainable
on a
custom
dataset
Supported frameworks
Example notebooks

Image classification

Yes

Yes

PyTorch, TensorFlow

Object detection

Yes

Yes

PyTorch, TensorFlow, MXNet

Semantic segmentation

Yes

Yes

MXNet

Instance segmentation

Yes

Yes

MXNet

Image embedding

Yes

No

TensorFlow, MXNet

Text classification

Yes

Yes

TensorFlow

Sentence pair classification

Yes

Yes

TensorFlow, Hugging Face

Question answering

Yes

Yes

PyTorch

Named entity recognition

Yes

No

Hugging Face

Text summarization

Yes

No

Hugging Face

Text generation

Yes

No

Hugging Face

Machine translation

Yes

No

Hugging Face

Text embedding

Yes

No

TensorFlow, MXNet

Tabular classification

Yes

Yes

LightGBM, CatBoost, XGBoost,
AutoGluon-Tabular,
TabTransformer, Linear Learner

Tabular regression

Yes

Yes

LightGBM, CatBoost, XGBoost,
AutoGluon-Tabular,
TabTransformer, Linear Learner