Yes. Hugging Face to ModelKit import is a core workflow. You provide the Hugging Face URL, and Jozu imports the model, packages it in a vendor-neutral format, and stores it in your enterprise registry. This import process gives AI/ML teams access to vetted and secured base models they can build on, without hand-crafting containers for each model. Jozu gives your organization a curated catalog of approved models from public sources like Hugging Face, scanned for security issues and license compliance. This replaces the “shadow” imports teams do by pulling from unvetted public sources to base their production-future work on.

Jozu isn't replacing your experiment tracking or ML pipeline tools - it's adding the security and deployment layer they lack. MLflow and Weights & Biases handle model development and experiment tracking. Kubeflow and cloud platforms (Sagemaker, Seldon, Vertex) handle training orchestration and inference serving. Jozu fills the gap between development and production with signed packaging, security scanning, policy enforcement, and optimized deployment.

Jozu’s Rapid Inference Containers (RIC) are optimized for rapid loading. Traditional approaches rebuild containers and transfer full model weights for each deployment. Jozu uses layer-deduplication (unchanged components aren't re-transferred), pre-configured inference containers, and optimized delivery to CPUs/GPUs. Real world deployments see 5-10x faster time to first token response compared to standard container, streaming, or NVIDIA NIM approaches. With AI agents and composite AI applications requiring larger, more complex deployments, this optimization becomes critical for production SLAs.

Large model packaging with ModelKits leverages OCI's layer-based architecture, which handles multi-gigabyte files efficiently. ModelKit sizes aren’t limited in Jozu’s built-in registry, but using an external registry may limit sizes based on their own arbitrary limits. Large models especially benefit from ModelKit packaging due to OCI’s layer deduplication: when you update a 70B parameter model, only the changed layers transfer - you're never re-uploading hundreds of gigabytes for configuration changes.

Multi-modal CI/CD works through ModelKits that package all model components - vision encoders, language models, fusion layers - as separate versioned layers. LLM pipeline deployment benefits from Jozu's optimized delivery, which is critical when models reach hundreds of gigabytes. Enterprise LLM management features include differential versioning (see exactly which components changed between versions), security scanning tailored for LLM-specific risks, and inference containers pre-configured for GPU serving.

ModelKit push pull operations use the kit CLI with familiar syntax. Developers run kit push registry.example.com/org/model:v1.0 to upload ModelKits and kit pull registry.example.com/org/model:v1.0 to retrieve them. CI/CD developer workflow integration works with any OCI-compliant registry using existing credentials. Automated model versioning tracks each push with SHA digests and optional semantic tags. From experiment trackers like MLflow, the pykitops Python library captures model lineage and generates ModelKits programmatically. Pipeline scripts simply call the CLI commands, and Jozu handles security scanning, signing, and validation automatically.

Yes. Automated retraining pipeline triggers work through your existing CI/CD systems. When training completes (whether in MLflow, Weights & Biases, or custom scripts), the pipeline calls kit pack to create a new ModelKit version, then kit push to upload it. CI/CD model updates trigger Jozu's security scanning automatically. Continuous model delivery can include automated validation gates - if security scans pass and attestations validate, the new version promotes through environments. Your pipeline orchestrates the workflow; Jozu provides the packaging, scanning, and deployment infrastructure. This replaces ad-hoc scripts with a governed, auditable process.