{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"Provides model serving and deployment functionality for machine learning models on Amazon SageMaker, integrating with SageMaker's core training and inference infrastructure.","skillfed_tags":["sagemaker","model-serving","aws-ml"],"use_cases":["Deploy trained PyTorch or ONNX models to SageMaker endpoints for real-time inference","Manage model serving infrastructure and endpoint lifecycle on AWS","Integrate model serving with SageMaker training pipelines for automated MLOps workflows","Monitor and track model performance in production using MLflow integration","Test model serving configurations locally before deploying to SageMaker"],"what_it_does":"SageMaker Serve is a Python package for deploying and serving machine learning models on Amazon SageMaker. It abstracts the complexity of model hosting by providing a unified interface to SageMaker's inference infrastructure, working alongside sagemaker-core and sagemaker-train to complete the ML lifecycle from training through production serving.\n\nThe package depends on boto3 for AWS API interaction, torch and onnxruntime for model inference, and includes testing infrastructure (pytest, tqdm, psutil) and monitoring tools (mlflow, tritonclient). It targets developers building end-to-end ML pipelines on AWS who need to move trained models into production endpoints without managing the underlying SageMaker deployment details directly.","worth_installing":"Yes, if you are building ML workflows on AWS SageMaker and need a unified serving layer. The package is actively maintained, has low install friction, carries a permissive Apache 2.0 license, and integrates with the SageMaker ecosystem. However, verify that its API and deployment model match your specific serving requirements before committing, as the fact sheet does not detail its exact interface or whether it supports your model types."},"id":"sagemaker-serve","links":{"html":"https://skillfed.io/packages/sagemaker-serve","md":"https://skillfed.io/packages/sagemaker-serve.md","pypi":"https://pypi.org/project/sagemaker-serve/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-11","license_spdx":null,"license_treatment":"permissive","name":"sagemaker-serve","python_support":"supports_current","summary":"SageMaker Serve package for model serving and deployment"},"popularity":{"monthly_downloads":1328127,"position":4051,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.19.0"}