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aws/amazon-sagemaker-examples

amazon-sagemaker-examples

Example πŸ““ Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

Builder

Amazon AWS

Amazon AWS

aws β€’ big-tech

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Created

Oct 23, 2017

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README Summary

This repository contains a comprehensive collection of Jupyter notebooks demonstrating how to use Amazon SageMaker for machine learning workflows. The examples cover building, training, and deploying ML models across various use cases and frameworks. It serves as a practical learning resource for developers wanting to implement machine learning solutions on AWS.

AI Dev Skills

Unmapped

Amazon SageMakerCloud Machine LearningModel TrainingModel DeploymentMLOpsDistributed TrainingHyperparameter TuningAutoMLFeature EngineeringModel MonitoringBatch TransformReal-time InferenceMulti-model EndpointsA/B Testing for MLData Processing Pipelines

Tags

Amazon SageMakerCloud Machine LearningModel TrainingModel DeploymentMLOpsDistributed TrainingHyperparameter TuningAutoMLFeature EngineeringModel MonitoringBatch TransformReal-time InferenceMulti-model EndpointsA/B Testing for MLData Processing PipelinesAutomated Machine LearningHyperparameter OptimizationMulti-algorithm ComparisonTime SeriesImageTextModel Training and DeploymentCloud APIBatch InferenceData Preprocessing WorkflowsTabularServerlessML Pipeline AutomationModel Performance MonitoringReal-time Model ServingJupyter Notebook

Taxonomy

Recent Activity

Updated 1 months ago

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Quality

production
Quality
high
Maturity
production

Categories

Model TrainingPrimaryObservability & MonitoringInference & ServingMLOps & InfrastructureDev Tools & AutomationCloud & PlatformsML Platform & InfrastructureData Science & AnalyticsOther AI / ML

PM Skills

Scale & ReliabilityDeveloper Platform

Languages

Jupyter Notebook100.0%

Timeline

Project created
Oct 23, 2017
Forked
Mar 22, 2026
Your last push
1 months ago
Upstream last push
8 days ago
Tracked since
Feb 24, 2026

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