Scalable AI starts with storage: Guide to model artifact strategies The best way to do this is to use Cloud Storage as the central, versioned, and secure source of truth for all model assets, such as safetensors, gguf, pkl, or joblib files This architectural pattern does more than just provide a convenient place to store files
Srcium — Artifact Management for Source Code AI Models Srcium is a unified artifact management platform for source code packages and AI models Version everything, store it reliably, and deploy with confidence — on the cloud or on your own infrastructure
Microsoft Build 2026: Securing code, agents, and models across the . . . Learn more about our solutions to help secure your code, secure your agents, and secure your models Today’s headlines reflect the tension around the power of AI models and the potential threat they pose when used to find and exploit vulnerabilities
GitHub - poojaagr21 secure-model-artifact-store: A cloud project to . . . Outcome This project demonstrates how cloud infrastructure for ML workloads can be operated reliably at scale using SRE best practices It reflects real-world responsibilities of a Cloud SRE, including infrastructure automation, monitoring, security enforcement, and incident
Cloudsmith: Cloud-Native Artifact Management Platform The foundation of any secure software supply chain is a single, observable home for all software artifacts Cloudsmith is a universal, cloud-native, enterprise-grade artifact management solution
Artifact Stores | MLflow AI Platform The artifact store is a core component in MLflow Tracking where MLflow stores (typically large) artifacts for each run such as model weights (e g a pickled scikit-learn model), images (e g PNGs), model and data files (e g Parquet file)
Data Security within AI Environments | CSA A data-centric AI security strategy prioritizes the protection of data across the entire AI lifecycle, beginning with collection and preprocessing and continuing through model training, inference, and storage