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Amazon Sagemaker Examples

Amazon Sagemaker Examples is a public Apache-2.0 repository of Jupyter notebooks that demonstrate how to build, train, customize, evaluate, deploy and operate machine-learning models with Amazon SageMaker. It also documents SageMaker-Core, a Python SDK that provides object-oriented access to SageMaker resources such as training jobs, models and endpoints.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Amazon Sagemaker Examples — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a machine-learning project needs a concrete SageMaker notebook reference for a lifecycle stage or a Python-oriented example of managing SageMaker resources.

Who it is for

  • ML practitioners learning or applying Amazon SageMaker workflows
  • Developers building Python-based SageMaker training, model and endpoint integrations
  • Teams looking for notebook references across model training, customization, evaluation and deployment

Documented uses

  • Find a notebook for a SageMaker machine-learning capability
  • Study workflows for managed, distributed or custom-container training
  • Explore foundation-model customization and evaluation techniques
  • Learn model deployment, inference and endpoint-management patterns
  • Use SageMaker-Core to work with SageMaker resources through Python
Before you choose

Requirements & limits

The repository is public and licensed under Apache-2.0. The documented notebook setup requires an AWS account, IAM configuration, a SageMaker Notebook Instance and an S3 bucket.

  • The examples are organized around the model lifecycle, from training through operationalization.
  • The repository covers both conventional ML workflows and foundation-model customization methods.
  • The notebooks are self-contained with related scripts, data and images in their folders.
  • Running outside SageMaker Notebook Instances requires IAM changes and installation of necessary libraries.
  • SageMaker-Core is presented as an object-oriented alternative to lower-level SageMaker resource management through Boto3.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the SageMaker example-notebook environment

Prepare the documented AWS resources and open the example notebooks in a SageMaker Notebook Instance.

Before you start

  • An AWS account
  • An IAM user and role configured for SageMaker access
  • An Amazon SageMaker Notebook Instance
  • An Amazon S3 bucket

Steps

  1. Create or identify the required AWS account, IAM user and role, SageMaker Notebook Instance and S3 bucket.

  2. Open the SageMaker Notebook Instance environment.

  3. In Jupyter, select the SageMaker Examples tab, or in JupyterLab, select the SageMaker logo.

  4. Choose an example notebook organized around the relevant machine-learning capability.

Expected outcome

The SageMaker example notebooks are available in the documented notebook environment for study and use.

Guide limitations

  • The documented setup does not establish that a particular notebook will complete successfully with a specific account, role, dataset or configuration.
  • Running notebooks outside a SageMaker Notebook Instance requires IAM role updates and installation of the necessary libraries.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Amazon Sagemaker ExamplesFind a notebook for a SageMaker machine-learning capabilityCloud, ServerThe repository is public and licensed under Apache-2.0. The documented notebook setup requires an AWS account, IAM configuration, a SageMaker Notebook Instance and an S3 bucket.
HyperposeCustomize pose-estimation architectures and datasets.Docker, Linux, macOS, Windows
Multi Task NlpDefine multiple tasks and their data and model settings in a YAML file.Linux, macOS, WindowsThe project is an open-source library under the Apache-2.0 license, distributed through its public GitHub repository.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
2 October 2026
Original website title
aws/amazon-sagemaker-examples · Official homepage title · retained in its source language
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