Posted by Mumbai: JFrog and AWS have announced a new integration between JFrog Artifactory and Amazon SageMaker – a Machine Learning (ML) service. The new integration will help companies build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.
It will allow the delivery of ML models alongside all other software development components in a modern DevSecOps workflow. This will make each model immutable, traceable, secure, and validated as it matures for release.
JFrog also unveiled new versioning capabilities for its ML Model management solution, which help ensure compliance and security are incorporated at every step of ML model development.
New JFrog and AWS integration
“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.
“The combination of Artifactory and Amazon SageMaker creates a single source of truth that indoctrinates DevSecOps best practices to ML model development in the cloud – delivering flexibility, speed, security, and peace of mind – breaking into a new frontier of MLSecOps,” added Hartman.
Governance policies within AI/ML
50% of data decision-makers cited applying governance policies within AI/ML as the biggest challenge to widespread usage, while 45% cited data and model security as the gating factor, revealed a recent Forrester survey.
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 organisational compliance.
JFrog’s new Amazon SageMaker integration allows organisations 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 organisation.
- 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.
The new JFrog and AWS integration is available now to customers and users of JFrog and Amazon SageMaker.