Capability Spotlight: ML Model Management
Establish a single system of record for ML models, integrating AI/ML development into your secure software supply chain.
Overview
Featured Benefits
Manage All Your Software Artifacts In One Place
Store and manage models alongside the other components that make up modern software applications for better visibility and insight into the status of your software development.
Bring DevOps Best Practices to ML Development
The DevOps practices developed over the past decade, such as artifact management, pipeline automation, and quality/feedback loops can now be applied to ML model management.
Ensure Integrity and Security of ML Models
Manage your models in a system that introduces important controls like RBAC, versioning, license, and security scanning so ML, Security, and DevOps teams feel confident about the models used.
Simplify Model Versioning Across Your SDLC
Leverage custom tags, name and timestamp versioning, and an advanced file system to ensure everyone uses the correct model version—enhancing clarity, context, and scalability.
Key Capabilities
- Secure, advanced AI/ML artifact registry
- Store and manage proprietary, modified OSS and third-party models
- Easy-to-use Python SDK for publishing all model artifacts into Artifactory
- Simplified, intuitive ML versioning
- Proxy Hugging Face, NVIDIA NGC for always available third-party models
- Detect malicious models and enforce license compliance
- Standardize MLOps processes across teams
- Integrated with ML tools such as Jupyter Notebooks, MLFlow, and Amazon Sagemaker