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aws/amazon-sagemaker-examples

amazon-sagemaker-examples

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

View on GitHub↗Upstream aws/amazon-sagemaker-examples↗

Builder

Amazon AWS

Amazon AWS

aws • big-tech

Stars

10,944

Using upstream star count

Forks

6,981

Using upstream fork count

Open Issues

0

Activity Score

0/100

0 commits in 30d

Created

Oct 23, 2017

Project creation date

README Summary

:exclamation::fire: Announcing SageMaker-Core: A New Python SDK for Amazon SageMaker :fire::exclamation:

Community Evaluation

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AI Dev Skills

Unmapped

A/B Testing for MLAmazon SageMakerAutoMLBatch TransformCloud Machine LearningData Processing PipelinesDistributed TrainingFeature EngineeringHyperparameter TuningMLOpsModel DeploymentModel MonitoringModel TrainingMulti-model EndpointsReal-time Inference

Tags

A/B Testing for MLAmazon SageMakerAutoMLBatch TransformCloud Machine LearningData Processing PipelinesDistributed TrainingFeature EngineeringHyperparameter TuningMLOpsModel DeploymentModel MonitoringModel TrainingMulti-model EndpointsReal-time InferenceAWSComputer VisionCurated ListData ScienceForkedLarge Language ModelsMachine LearningPlanning / CoTPythonReal-Time / StreamingSageMakerSynthetic DataTutorial

Taxonomy

AI Trends

MLOpsAutoMLDistributed TrainingModel MonitoringML Pipeline Automation

category

Cloud & PlatformsFoundation ModelsAI AgentsModel TrainingInference & ServingComputer VisionMLOps & InfrastructureLearning ResourcesData Science & Analytics

Deployment Context

Cloud APIServerless

Modalities

TabularTextImageTime Series

Skill Areas

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

tag

AWSComputer VisionCurated ListData ScienceForkedLarge Language ModelsMLOpsMachine LearningPlanning / CoTPythonReal-Time / StreamingSageMakerSynthetic DataTutorial

Use Cases

Model Training and DeploymentAutomated Machine LearningHyperparameter OptimizationBatch InferenceReal-time Model ServingML Pipeline AutomationModel Performance MonitoringMulti-algorithm ComparisonData Preprocessing Workflows

Recent Activity

Updated 3 months ago

7 Days

0

30 Days

0

90 Days

0

Upgrade sm-mlflow_pipeline.ipynb to sagemaker v3 (#4873)

pdifranc • Feb 24, 2026

58a3cd5

Quality

production
Quality
high
Maturity
production

Categories

Cloud & PlatformsPrimaryInference & ServingMLOps & InfrastructureLearning ResourcesData Science & AnalyticsFoundation ModelsAI AgentsModel TrainingComputer VisionML Platform & InfrastructureOther AI / ML

PM Skills

Scale & ReliabilityData & EvaluationAI-Native Architecture

Languages

Jupyter Notebook100.0%

Timeline

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

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