JFrog for AI & ML¶

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As AI and ML workloads mature from experiments into production pipelines, teams face the same artifact management challenges they solved for software: versioning, distribution, security, and governance — but now for models, datasets, and ML libraries.

JFrog Artifactory provides a universal platform to manage AI/ML artifacts alongside traditional software artifacts, giving MLOps teams the same rigor as DevOps teams.

Why JFrog for AI/ML?¶

Challenge	JFrog Solution
Model files not versioned	Store models as artifacts with semantic versions and metadata
AI packages have CVEs	Xray scans PyPI, Conda, and Hugging Face packages
Training pipeline pulls from public internet	Cache and proxy PyPI, Conda, Hugging Face Hub via Remote repos
No audit trail for deployed models	Build info links deployed model to training run, dataset, commit
Malicious AI packages slipping in	Curation blocks untrusted AI packages before they reach developers

JFrog AI/ML Capabilities¶

Capability	Product	Description
ML Model Storage	Artifactory Generic/ML Repos	Store versioned `.onnx`, `.gguf`, safetensors, `.pkl` files
Package Caching	Artifactory Remote Repos	Proxy PyPI, Hugging Face Hub, Conda
CVE Scanning	Xray	Scan AI packages and model dependencies
Package Curation	JFrog Curation	Block vulnerable or unvetted AI packages
Build Traceability	Artifactory Build Info	Link models to training runs and dataset versions
Runtime Security	JFrog Advanced Security	Detect threats in deployed AI systems

Tutorials in This Section¶

Tutorial	Description
ML Model Repositories	Store and version ML models as first-class artifacts
MLOps Pipeline with JFrog	Integrate JFrog into your training-to-deployment pipeline
AI/ML Security with Xray	Scan and block vulnerable AI packages
Curating AI/ML Packages	Approve/block AI library versions organization-wide

Supported AI/ML Artifact Types¶

Artifact Type	Format	Storage in JFrog
PyTorch models	`.pt`, `.pth`	Generic or ML repo
ONNX models	`.onnx`	Generic repo
Hugging Face models	`safetensors`, `.bin`	Generic repo
GGUF (llama.cpp)	`.gguf`	Generic repo
Scikit-learn models	`.pkl`, `.joblib`	Generic repo
Python packages	`.whl`, `.tar.gz`	PyPI repo
Conda environments	`.tar.bz2`	Conda repo

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