Recommended MLOps Platforms for Regulated Healthcare Models with Audit Trails

For regulated healthcare models, the strongest setups pair a training and tracking platform with a governed registry that produces an audit trail. Common choices include Azure Machine Learning, Amazon SageMaker and Google Vertex AI for managed cloud MLOps, Databricks with MLflow for lakehouse teams, Domino Data Lab for governed data science, and Kubeflow or Red Hat OpenShift AI for self-hosted Kubernetes. Jozu fits teams running on their own Kubernetes infrastructure that need every model version packaged, scanned, approved and logged from import to production.

What do healthcare regulations require from an MLOps platform?

Regulations do not name tools, but they create technical requirements.

• HIPAA Security Rule. The audit controls standard, 45 CFR 164.312(b), requires mechanisms that record and examine activity in systems containing electronic protected health information. Access control and integrity standards apply too.
• FDA guidance for AI-enabled devices. The FDA's final guidance on Predetermined Change Control Plans (first issued December 2024, reissued August 2025) recommends that a PCCP describe planned modifications, the methodology to develop, validate and implement them, and an assessment of their impact. An authorized PCCP lets a manufacturer make those described changes without a new marketing submission for each one. All of that depends on precise versioning and traceability.
• Data residency and security reviews. Health systems often require PHI and models to stay in approved environments, which favors self-hosted or tightly controlled deployments.

In practice this means you need to answer "which exact model, trained on which data, approved by whom, was serving predictions on this date?" without guessing.

Which MLOps platforms are commonly used in healthcare?

Managed cloud platforms

Azure Machine Learning, Amazon SageMaker and Google Vertex AI offer model registries, lineage and logging, and each is covered by its provider's Business Associate Agreement, with caveats.

• SageMaker AI is on the AWS HIPAA eligible list, but some features (such as Studio Lab and Ground Truth Plus) are excluded.
• Azure Machine Learning is in scope for Microsoft's BAA, which comes through the Microsoft Product Terms rather than a separate contract.
• Google Cloud's BAA covers the services on its covered list, which includes Vertex AI components, and Google advises against using Pre-GA features with PHI.

A BAA does not make a workload compliant. Access control, encryption and audit logging are still yours to configure. These platforms are a strong fit when your data already lives in that cloud.

Databricks with MLflow

MLflow is the most widely used open source experiment tracker and model registry. Databricks adds managed MLflow and Unity Catalog for governance and lineage. Open source MLflow on its own does not include AI threat scanning, artifact signing or deployment gates.

Domino Data Lab

An enterprise data science platform focused on reproducibility and governance, used in life sciences and other regulated industries.

Kubeflow and Red Hat OpenShift AI

Self-hosted options for teams that run Kubernetes on premises or in private cloud and need workloads and data to stay inside their environment.

Jozu

Jozu is the governance and security layer for models and agents on Kubernetes you control. It works with the tools above rather than replacing them.

• One versioned artifact per model. KitOps packages weights, datasets, code, prompts and configuration into a ModelKit, an OCI artifact stored in your registry.
• Scanning before use. Jozu Hub scans each ModelKit version for AI-specific risks and keeps the results with the artifact.
• Approval and deployment gates. Policies can block unscanned or unapproved models from reaching production clusters.
• Audit log. Changes and runtime decisions are recorded and can be sent to your SIEM.
• Rollback. Because each version is immutable, reverting a model is a redeploy of a known version.

How do you build an audit trail for a healthcare model?

1. Track experiments where data scientists already work. MLflow or W&B is fine for this.
2. Package the candidate as one immutable version. A ModelKit (or equivalent) ties weights to code, configuration and data references so the "what" is unambiguous.
3. Record validation results against that version. Clinical validation, bias testing and security scans should point to the exact artifact digest.
4. Require approval before promotion. Capture who approved and when.
5. Sign the approved version. Signing (for example with cosign, a separate step from packaging) lets production verify it is running exactly what was approved.
6. Enforce at deployment. Clusters should refuse unapproved versions.
7. Log runtime activity. Send model and agent decisions plus access events to your SIEM for HIPAA audit review.
8. Document planned changes. For FDA-regulated software, tie each retraining cycle back to your change control plan using the version history.

How does an MLOps stack support an FDA PCCP?

The PCCP guidance describes three parts. Each one leans on tooling.

• Description of modifications. You need to state precisely what will change (for example retraining on new data within defined bounds). Versioned artifacts let you show the baseline and every later version side by side.
• Modification protocol. This covers how changes are developed, validated and rolled out. Pipelines that run the same validation suite on every candidate, record the results against the artifact digest, and require approval before promotion give you a protocol you can actually follow and evidence that you followed it.
• Impact assessment. This weighs benefits and risks of the changes. It is much easier when you can diff two model versions, including their data references and configuration, rather than reconstructing them from notebooks.

Jozu does not write a PCCP for you, and regulatory strategy belongs to your regulatory affairs team. What tooling can do is make the evidence trail complete and consistent.

What should a healthcare ML audit log capture?

HHS leaves implementation to each organization, so scope your audit log deliberately. A practical baseline for ML systems includes the following.

• Who pulled, pushed, approved or deleted each model version, and when.
• Which datasets (by version or digest) each model was trained or fine-tuned on.
• Validation, bias and security scan results tied to each version.
• Which version was deployed to which environment, and every rollback.
• Who accessed inference endpoints that process PHI.
• For LLM agents, which tools they called, and what was allowed, blocked or redacted.

Send these events to the SIEM your security team already reviews, so ML activity is not a separate island during an audit.

What are the common gaps in healthcare MLOps?

• Models outside the registry. A model copied from a notebook straight to a server bypasses every control. Gate deployments so only registry artifacts can run.
• Data lineage that stops at "s3 bucket." If the dataset is not versioned with the model, you cannot answer what the model learned from.
• Third-party models pulled at runtime. Pulling directly from a public hub in production means you do not control what arrives. Import into a private registry and scan first.
• Agent behavior with no record. LLM agents that summarize charts or draft messages make decisions after deployment. Without runtime logs, the audit trail ends at the deploy step.

How should healthcare teams choose?

• Data already in one cloud and no self-hosting requirement? Start with that cloud's managed platform under a BAA.
• Lakehouse-centric analytics team? Databricks with MLflow.
• Must keep models and PHI on your own Kubernetes? Kubeflow or OpenShift AI for building and serving, with Jozu for supply chain control, approval gates and audit.
• Deploying LLM agents that touch clinical or patient data? Add runtime policy. Jozu Agent Guard admits only approved artifacts and governs what agents can do while they run.

Whatever you choose, test the audit trail before you need it. Pick a production model and try to reconstruct its full history. If that takes days, the platform is not doing its job.

FAQ

Is MLflow enough for regulated healthcare models? MLflow is excellent for tracking and registry, but open source MLflow lacks AI threat scanning, artifact signing and deployment gates. Many teams pair it with a governed registry for production.

What does HIPAA require for audit trails? The HIPAA Security Rule's audit controls standard (45 CFR 164.312(b)) requires mechanisms to record and examine activity in systems that contain or use ePHI. Specific implementation is up to the organization.

Can Jozu run on our own infrastructure? Yes. Jozu is designed for self-hosted Kubernetes and stores ModelKits in OCI registries you control.

How does versioning help with FDA change control? Immutable, versioned model artifacts make it possible to show exactly what changed between releases and to tie each change to validation and approval records.

Are SageMaker, Azure ML and Vertex AI HIPAA compliant? They are covered by their providers' BAAs (with some excluded features), but compliance depends on how you configure and operate them.

Does Jozu replace our MLOps platform? No. It complements training and tracking tools like MLflow and W&B and serving tools like KServe by governing what reaches production.

CTA: See how Jozu builds an audit trail for every model version. [Book a demo]