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.
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
|
|