JFrog Introduces Native Integration for Hugging Face, Delivering Robust Support for ML Models to Harmonize DevOps, Security and AI
JFrog Introduces Native Integration for Hugging Face, Delivering Robust Support for ML Models to Harmonize DevOps, Security and AI
DevOps teams, ML Engineers and Data Scientists can now store, secure, govern and manage AI components with confidence, including industry-first platform for detecting malicious ML models
JFrog Ltd. (“JFrog”) (Nasdaq: FROG), the Liquid Software company and creators of the JFrog Software Supply Chain Platform, today introduced ML Model Management capabilities, an industry-first set of functionality designed to streamline the management and security of Machine Learning [ML] models. The new ML Model Management capabilities in the JFrog Platform bring AI deliveries in line with an organization’s existing DevOps and DevSecOps practices to accelerate, secure and govern the release of ML components.
“Today, Data Scientists, ML Engineers, and DevOps teams do not have a common process for delivering software. This can often introduce friction between teams, difficulty in scale, and a lack of standards in management and compliance across a portfolio,” said Yoav Landman, Co-founder and CTO, JFrog. "Machine learning model artifacts are incomplete without Python and other packages they depend on and are often served using Docker containers. Our customers already trust JFrog as the gold standard for artifact management and DevSecOps processes. Data scientists and software engineers are the creators of modern AI capabilities, and already JFrog-native users. Therefore, we look at this release as the next logical step for us as we bring machine learning model management, as well as model security and compliance, into a unified software supply chain platform to help them deliver trusted software at scale in the era of AI.”
AI and ML usage continues to grow rapidly. IDC Research indicates the worldwide AI/ML market, including software, hardware, and services, is forecast to grow 19.6 percent to over $500B in 2023. However, as more ML models are being moved to production, the end users often face challenges including cost, lack of automation, lack of expertise, and ability to scale.
"It can take significant time and effort to deploy ML models into production from start to finish. However, even once in production, users face challenges with model performance, model drift, and bias," said Jim Mercer, Research Vice President, DevOps & DevSecOps, IDC. So, having a single system of record that can help automate the development, ongoing management, and security of ML Models alongside all other components that get packaged into applications offers a compelling alternative for optimizing the process."
Using JFrog’s new ML Model Management capabilities organizations can:
- Proxy the popular public ML repository Hugging Face to cache open source AI models companies rely on, bringing them closer to development and production, protecting them from deletion or modification.
- Detect and block use of malicious ML models.
- Scan ML model licenses to ensure compliance with company policies.
- 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.
“Increasing numbers of organizations are starting to incorporate ML models into their applications and with several government regulations requiring software vendors to list exactly what’s inside their software, we believe it won’t be long before these guidelines grow to include ML and AI models as well,” said Yossi Shaul, SVP Product and Engineering, JFrog. “We’re excited to give customers an easy way to proxy, store, secure, and manage models alongside their other software components to help accelerate their pace of innovation while remaining well-positioned for tomorrow’s demands.”
First published: 11:01, 14.09.23
