{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"Trains and deploys machine learning models on Amazon SageMaker using popular frameworks like Apache MXNet, TensorFlow, and Amazon's built-in algorithms, or custom Docker containers.","skillfed_tags":["aws-sagemaker","ml-training","distributed-computing"],"use_cases":["Train deep learning models using TensorFlow or Apache MXNet on SageMaker's managed infrastructure without managing compute directly.","Deploy trained models to SageMaker endpoints for real-time inference or batch predictions.","Use Amazon's pre-built, optimized algorithms for common ML tasks like classification or regression.","Integrate custom training code packaged in Docker containers with SageMaker's distributed training and monitoring.","Track and manage ML experiments using MLflow integration for reproducibility and model comparison."],"what_it_does":"sagemaker-train is the AWS SageMaker Python SDK's training module, a library for programmatically training and deploying machine learning models on Amazon's managed SageMaker service. It abstracts the complexity of distributed training, GPU provisioning, and model hosting by providing a Python interface to SageMaker's capabilities. The package supports training with popular frameworks like Apache MXNet and TensorFlow, Amazon's optimized built-in algorithms, and custom algorithms packaged in SageMaker-compatible Docker containers.\n\nThe library is actively maintained, tested on Python 3.10\u20133.12, and depends on a stack of 12 runtime packages including sagemaker-core, mlflow, pydantic, and paramiko. It includes telemetry by default (opt-out available via configuration) and requires AWS credentials and appropriate IAM permissions to function. The package is in Alpha status and is designed for developers building ML workflows on AWS infrastructure.","worth_installing":"Yes, if you are building ML workflows on AWS and want a managed training and deployment platform. The package is actively maintained, has low install friction, and integrates well with SageMaker's ecosystem. However, verify the license terms (Apache 2.0 is mentioned in the description but not confirmed in metadata) and ensure your AWS account has the necessary IAM permissions before relying on it. Not suitable if you need to train models outside AWS or prefer a cloud-agnostic ML framework."},"id":"sagemaker-train","links":{"html":"https://skillfed.io/packages/sagemaker-train","md":"https://skillfed.io/packages/sagemaker-train.md","pypi":"https://pypi.org/project/sagemaker-train/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-11","license_spdx":null,"license_treatment":"unclear","name":"sagemaker-train","python_support":"supports_current","summary":"Open source library for training and deploying models on Amazon SageMaker."},"popularity":{"monthly_downloads":1316222,"position":4069,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.19.0"}