Get Started with AI/ML in JFrog
The system of record for your enterprise AI supply chain.
JFrog ML solution acts as a centralized hub that proactively secures every AI workload — from third-party downloads to custom in-house models — giving you the visibility needed to eliminate Shadow AI.
With security and compliance enforced proactively at the gate, your teams can safely accelerate the development of trusted AI applications without compromising control.
The diagram above illustrates this secure AI supply chain in action. It maps the flow of AI assets through the JFrog Platform, how malicious and non-compliant models are detected and blocked at the perimeter, while trusted assets are cataloged and made available for rapid development.
How Does It Work?
Gain Full AI Visibility
You cannot govern what you cannot see. JFrog automatically scans your repositories and builds to uncover every existing AI model and external API currently in your Artifactory. By revealing Shadow AI, you can assess immediate risks and establish a clean, trusted baseline for your AI operations journey.
Your Single Source of Truth for AI
Unify every AI asset, including commercial APIs (like OpenAI), open-source models (like Hugging Face), and MCP servers, into one secure, centralized hub. Provide developers with self-service access to approved tools while ensuring strict security and compliance.
From Notebook to Production
Bridge the gap between experimentation and production with a simplified workflow to log, build, and deploy your custom models. By automating the transition from code to a production-ready artifact, you ensure reproducibility without the usual infrastructure headaches.
Maintain Trust in Live Models
Models degrade over time as real-world data changes. JFrog tracks real-time model health and automatically detects data drift. By monitoring live traffic against your training baseline, you ensure your AI remains accurate and trustworthy without constant manual checking.
Accelerate Feature Management
Simplify the data preparation process by transforming raw data into a centralized library of governed features. By defining your data logic once using simple SQL, you ensure the exact same features used for training are available for production, eliminating costly data mismatch bugs.
Where to Start?
The JFrog AI/ML guide walks you through key concepts, tutorials, or best practices. Either use the search bar or select from the options below.
Updated 19 days ago