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JFrog and AWS Accelerate Secure Machine Learning Development

Kelly Hartman, SVP, Global Channels and Alliances, JFrog

Forrester Survey: 50% of Data Decision-Makers Identify Governance Policies as Primary Challenge in AI/ML Adoption, While 45% Emphasize Data and Model Security

JFrog Ltd. introduces an integration with Amazon SageMaker, enabling seamless incorporation of machine learning (ML) models into DevSecOps workflows. The integration, pairing JFrog Artifactory with Amazon SageMaker, ensures that ML models are delivered alongside other development components, maintaining immutability, traceability, security, and validation throughout their maturity for release. Additionally, JFrog enhances its ML Model management solution with new versioning capabilities to uphold compliance and security throughout the development process.

“As more companies begin managing big data in the cloud, DevOps team leaders are asking how they can scale data science and ML capabilities to accelerate software delivery without introducing risk and complexity” 

Kelly Hartman, SVP, Global Channels and Alliances, JFrog

“As more companies begin managing big data in the cloud, DevOps team leaders are asking how they can scale data science and ML capabilities to accelerate software delivery without introducing risk and complexity,” said Kelly Hartman, SVP, Global Channels and Alliances, JFrog.”

According to a recent Forrester survey 50 percent of data decision-makers cited applying governance policies within AI/ML as the biggest challenge to widespread usage, while 45 percent cited data and model security as the gating factor. JFrog’s Amazon SageMaker integration applies DevSecOps best practices to ML model management, allowing developers and data scientists to expand, accelerate, and secure the development of ML projects in a manner that is enterprise-grade, secure, and abides by regulatory and organizational compliance.

JFrog’s new Amazon SageMaker integration allows organizations to:

  • Maintain a single source of truth for data scientists and developers, ensuring all models are readily accessible, traceable, and tamper-proof.
  • Bring ML closer to the software development and production lifecycle workflows, protecting models from deletion or modification.
  • Develop, train, secure and deploy ML models.
  • Detect and block the use of malicious ML models across the organization.
  • Scan ML model licenses to ensure compliance with company policies and regulatory requirements.
  • Store home-grown or internally augmented ML models with robust access controls and versioning history for greater transparency.
  • Bundle and distribute ML models as part of any software release.

“Traditional software development processes and machine learning stand apart, lacking integration with existing tools,” said Larry Carvalho, Principal and founder of RobustCloud.”

Along with its Amazon SageMaker integration, JFrog unveiled new versioning capabilities for its ML Model Management solution that incorporate model development into an organization’s DevSecOps workflow to increase transparency around each model version so developers, DevOps teams, and data scientists can ensure the correct, secure version of a model is utilized.

The JFrog integration with Amazon SageMaker, available now for JFrog customers and Amazon SageMaker users, ensures all artifacts consumed by data scientists or used to develop ML applications are pulled from and saved in JFrog Artifactory.

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