Amazon-Sagemaker-Examples

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amazon-sagemaker-examples is an Open Source Research & Education tool that provides Jupyter notebooks to demonstrate building, training, and deploying machine learning models using Amazon SageMaker. It offers practical examples for ML practitioners and data scientists.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is amazon-sagemaker-examples?

The amazon-sagemaker-examples repository offers a comprehensive collection of Jupyter notebooks designed to guide users through the process of building, training, and deploying machine learning models with Amazon SageMaker. These examples cover the entire ML lifecycle, from data preparation and model building to deployment, monitoring, and advanced topics like Generative AI and MLOps. The repository also introduces SageMaker-Core, a new Python SDK that simplifies interaction with SageMaker resources through an object-oriented interface and resource chaining. It's an invaluable resource for ML practitioners seeking to customize AWS primitives for their workloads, providing detailed documentation, code samples, and instructions for running examples both within and outside SageMaker Notebook Instances.

Best used for

Ideal for developers and students who need to understand and implement machine learning workflows, practice with Amazon SageMaker features, and explore advanced topics like Generative AI and MLOps. Especially valuable for those seeking hands-on experience with AWS ML services through practical, documented examples.

Common actions

learn machine learning

build ML models

deploy ML models

practice SageMaker

implement MLOps

"AI Agents"github copilotface swappingworkflowsopen-sourceautomated workflowdeepfakelow-code/no-codecollaboration

Capabilities

Key features

Jupyter notebook examples
End-to-end ML lifecycle
Data preparation examples
Model build/train examples
Deploy/monitor models
Generative AI examples
MLOps implementation

Target Audience

developerstudent

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is SageMaker-Core and how does it improve SageMaker interaction?

SageMaker-Core is a new Python SDK that provides an object-oriented interface for SageMaker resources like TrainingJob, Model, and Endpoint. It simplifies code management through resource chaining, abstracts low-level details, and enhances the developer experience with auto-completion, comprehensive documentation, and type hints, offering an improvement over Boto3.

How do these examples help with different stages of the ML lifecycle?

The notebooks cover the entire ML lifecycle, including preparing data by handling missing values and scaling features, building and training models with SageMaker's managed services, and deploying and monitoring models using various inference options and model monitoring tools like Amazon CloudWatch Logs.

Can these examples be run outside of an Amazon SageMaker Notebook Instance?

Yes, while these examples are automatically loaded into SageMaker Notebook Instances, they can be run outside with minimal modifications. Users would typically need to update IAM role definitions and install necessary libraries to execute the notebooks in their preferred environment.

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Coding & Development › Code Assistants Coding & Development › Open Source & Models

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