langchain/docs/extras/integrations/providers/sagemaker_endpoint.mdx

# SageMaker Endpoint

>[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a system that can build, train, and deploy machine learning (ML) models with fully managed infrastructure, tools, and workflows.

We use `SageMaker` to host our model and expose it as the `SageMaker Endpoint`.


## Installation and Setup

```bash
pip install boto3
```

For instructions on how to expose model as a `SageMaker Endpoint`, please see [here](https://www.philschmid.de/custom-inference-huggingface-sagemaker). 

**Note**: In order to handle batched requests, we need to adjust the return line in the `predict_fn()` function within the custom `inference.py` script:

Change from

```
return {"vectors": sentence_embeddings[0].tolist()}
```

to:

```
return {"vectors": sentence_embeddings.tolist()}
```



We have to set up following required parameters of the `SagemakerEndpoint` call:
- `endpoint_name`: The name of the endpoint from the deployed Sagemaker model.
    Must be unique within an AWS Region.
- `credentials_profile_name`: The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
    has either access keys or role information specified.
    If not specified, the default credential profile or, if on an EC2 instance,
    credentials from IMDS will be used.
    See [this guide](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html).

## LLM

See a [usage example](/docs/integrations/llms/sagemaker).

```python
from langchain import SagemakerEndpoint
from langchain.llms.sagemaker_endpoint import LLMContentHandler
```

## Text Embedding Models

See a [usage example](/docs/integrations/text_embedding/sagemaker-endpoint).
```python
from langchain.embeddings import SagemakerEndpointEmbeddings
from langchain.llms.sagemaker_endpoint import ContentHandlerBase
```