---
id: sagemaker-core
version: "2.19.0"
license: unclear
license_treatment: permissive
maintenance: active
---

# sagemaker-core — An python package for sagemaker core functionalities

License: permissive · Maintenance: active · Downloads: 8.3M/mo

## What it is and what it does

sagemaker-core is a Python SDK that wraps Amazon SageMaker's APIs in an object-oriented interface, released in 2024 and actively maintained. It replaces low-level API calls with dedicated resource classes, type hints, and intelligent defaults, allowing developers to chain SageMaker resources together and focus on model building rather than infrastructure plumbing. The package depends on boto3 for AWS communication, pydantic for validation, and a suite of data science libraries (pandas, numpy, scipy) for model work.

The SDK abstracts state management and polling logic, handles resource transitions automatically, and provides IDE auto-completion. It targets Python 3.10 or later and carries 24 runtime dependencies. The package is in Alpha status, meaning its API may still evolve, but it has no known security vulnerabilities and is actively developed.

Use it for:

- Build SageMaker training pipelines using object-oriented resource chaining instead of manual API calls.
- Deploy ML models to SageMaker endpoints with type-safe, IDE-assisted code and intelligent defaults.
- Manage SageMaker notebooks, processing jobs, and feature stores programmatically with less boilerplate.
- Migrate from lower-level boto3 calls to a higher-level, more maintainable SDK interface.
- Prototype ML workflows quickly using auto-completion and comprehensive type hints in your IDE.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

sagemaker-core provides an object-oriented Python interface to Amazon SageMaker resources with full API parity, resource chaining, and type hints for building and deploying machine learning models.

Yes, with conditions. Install if you are building on AWS SageMaker and want a higher-level, type-safe interface over raw boto3 calls. The active maintenance, permissive license, and zero known vulnerabilities are strong signals. However, the Alpha status means the API may change; pin a version and monitor releases. The 24 dependencies are all reputable, but verify they fit your environment constraints before committing to production.

## Install

pip install sagemaker-core
uv add sagemaker-core
poetry add sagemaker-core

## Installing sagemaker-core

Before you install: Low friction install with a pure-wheel distribution. Active maintenance as of 4 days ago with recent commits and 2259 repository stars. Requires Python 3.10 or later. Pulls in 24 runtime dependencies including boto3, pydantic, and scientific libraries; all are widely-used packages with established track records.

License in practice: Licensed under Apache 2.0 (permissive). No restrictions on commercial use, modification, or distribution; suitable for proprietary projects provided you include the license notice.

Quickstart:

pip install sagemaker-core

import sagemaker_core
from sagemaker_core import SageMakerSession

session = SageMakerSession()

Requires Python 3.10 or later; requires AWS credentials configured in your environment.

Verify before relying:

- Whether resource chaining works seamlessly across all SageMaker resource types or has documented limitations.
- Performance characteristics when managing large numbers of resources or long-running training jobs.
- Stability guarantees given the 'Alpha' development status and recent first release (2024-07-18).

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 8.3M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

sagemaker python sdk, aws machine learning api, sagemaker resource management, ml model deployment aws, sagemaker workflow automation, object-oriented sagemaker interface, aws ml infrastructure code, aws-sagemaker, ml-infrastructure, object-oriented-api

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