text: EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerSageMaker EndpointOn this pageSageMaker EndpointAmazon 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 Setupâ€‹pip install boto3For instructions on how to expose model as a SageMaker Endpoint, please see here. 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 fromreturn {"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.