Amazon SageMaker

By leveraging JFrog Artifactory and Amazon SageMaker together, machine learning (ML) models can be delivered alongside all other software development components.

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Amazon SageMaker Features

Single Source of Truth for ML Artifacts

JFrog’s Amazon SageMaker integration enables organizations to maintain a single source of truth for all machine learning artifacts. Data scientists and developers can access, trace, and manage models, datasets, and other resources securely in one centralized repository. This ensures that all models are readily accessible, streamlining collaboration across teams and preventing unauthorized modifications.

Seamless Integration into Software Development Workflows

By integrating JFrog with Amazon SageMaker, organizations can bring machine learning closer to their software development and production lifecycle workflows. This integration protects models from accidental deletion or unauthorized modification, ensuring that they are securely managed throughout their lifecycle. Additionally, ML models can be bundled and distributed as part of any software release, aligning development and deployment processes.

Comprehensive Model Development and Deployment

With JFrog’s integration, you can develop, train, secure, and deploy machine learning models seamlessly within the Amazon SageMaker environment. Artifacts are stored with robust access controls and versioning history, providing transparency and traceability. This end-to-end support ensures that your ML models are handled with the same rigor as any other critical software component.

Robust Version Control and Access Management

With JFrog Artifactory, organizations can store home-grown or internally augmented ML models with comprehensive versioning history and robust access controls. This ensures greater transparency and accountability in how models are developed, trained, and deployed. The version control features allow teams to track changes, revert to previous versions if necessary, and manage permissions to ensure that only authorized personnel can access or modify models.

End-to-End Model Lifecycle Management

The JFrog and SageMaker integration supports the entire machine learning model lifecycle—from development and training to securing and deploying ML models. By leveraging JFrog Artifactory, organizations can manage the distribution of models as part of any software release, ensuring that all components, including ML models, are consistently and securely deployed. This end-to-end management capability ensures that machine learning becomes an integral and secure part of your software production environment.

What Artifactory and Xray mean for your hybrid infrastructure

Related Resources

Documentation

How to Use JFrog Artifactory with Amazon SageMaker

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Blog Post

Integrating JFrog Artifactory with Amazon SageMaker

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Webinar

DevSecOps in the Era of AI/ML Model Development with AWS

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Press Release

JFrog and AWS Accelerate Secure Machine Learning Development

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Amazon SageMaker FAQs

What are the benefits of integrating JFrog Artifactory with Amazon SageMaker?

Integrating JFrog Artifactory with Amazon SageMaker allows you to efficiently manage, version, and distribute machine learning models, datasets, and other artifacts. This integration ensures that your ML workflows are streamlined, from training to deployment, while maintaining control over all dependencies and enabling collaboration across teams.

How do I set up JFrog Artifactory to manage models and datasets used in SageMaker?

To set up JFrog Artifactory for managing SageMaker artifacts, you need to configure Artifactory to store your machine learning models, datasets, and Docker images. These artifacts can then be pulled into SageMaker for training and deployment. Detailed setup instructions are available in the JFrog and AWS documentation.

Can JFrog Xray be used to scan machine learning models for security vulnerabilities before deploying them in SageMaker?

Yes, JFrog Xray can scan machine learning models, datasets, and container images stored in Artifactory for security vulnerabilities and compliance issues. This ensures that only secure and compliant artifacts are deployed in Amazon SageMaker, enhancing the security of your ML workflows.

How does version control work for machine learning models in the JFrog and SageMaker integration?

JFrog Artifactory provides robust version control for machine learning models and other artifacts. Each version of a model or dataset is stored with unique metadata, making it easy to track changes, roll back to previous versions, and ensure reproducibility in your SageMaker workflows.

What kind of artifacts can be managed in JFrog Artifactory for use with SageMaker?

JFrog Artifactory can manage a wide range of artifacts for use with Amazon SageMaker, including machine learning models, datasets, Jupyter notebooks, Docker images, and Python packages. This flexibility allows you to centralize the management of all resources required for your ML projects in one place.

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About Amazon SageMaker

Amazon SageMaker is a fully managed AWS service that brings together a broad set of tools to enable high-performance, low-cost ML for any use case. With SageMaker, you can build, train and deploy ML models at scale using tools like notebooks, debuggers, profilers, pipelines, MLOps, and more – all in one integrated development environment (IDE).

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