On-prem MLOps in a hospital: advice needed for production monitoring of self-built and vendor models? [D]
Our take
The hospital’s predicament, as outlined in this Reddit post, highlights a growing tension in the AI landscape: the need for robust, auditable MLOps infrastructure in highly regulated environments. This isn't just about deploying models; it's about ensuring their ongoing safety, fairness, and efficacy, especially when those models directly impact patient care. The challenge of needing comprehensive production monitoring – drift, bias, custom metrics, and alerting – is compounded by the reality of increasingly complex model sourcing, with a significant portion running on vendor infrastructure. This situation echoes concerns raised in "Structured Evaluation Pipelines to Improve Your AI Workflows" Structured Evaluation Pipelines to Improve Your AI Workflows, where the importance of standardized evaluation frameworks is emphasized—a critical precursor to the kind of rigorous monitoring this hospital is seeking. The need for consistent monitoring across both in-house and vendor-managed models represents a significant architectural hurdle, requiring a solution that transcends traditional, platform-centric MLOps tools.
The hospital’s evaluation of ClearML and OpenShift AI is sensible given their existing OpenShift infrastructure, but their frustration with the lack of production monitoring capabilities is entirely justified. While both platforms excel in development and deployment, the core issue lies in the operationalization and governance of models in production. The proposed solution of layering Evidently AI on top of a chosen platform – essentially building a custom monitoring pipeline – is a pragmatic one, but it also underscores the limitations of current MLOps tooling. The requirement to ingest input/output data from vendor models, a legal necessity given MDR and the EU AI Act, further complicates matters, demanding a flexible and adaptable monitoring system. This is particularly relevant given the ongoing discussions around AI transparency and accountability, as explored in "Claude Now Watermarks Everything It Makes" Claude Now Watermarks Everything It Makes, which highlights the broader industry push toward establishing provenance and traceability for AI-generated content—a need that extends directly to model outputs in critical applications.
The hospital’s self-service platform approach, with its focus on boundary policies and per-project dashboards, is a smart move to avoid becoming a bottleneck while maintaining central control. Empowering individual teams to monitor their models, without requiring constant IT intervention, is crucial for agility and innovation. However, this also necessitates a robust alerting system with clearly defined ownership, as highlighted by the post. The success of such a platform hinges on the ability to provide consistent monitoring experiences regardless of where the model is running – on-prem or with a vendor. This points towards a future where MLOps platforms need to offer a more unified view of the entire model lifecycle, integrating seamlessly with external systems and providing standardized monitoring capabilities across diverse deployment environments. Offering free compute, as discussed in "I have a mid-sized GPU cluster and was thinking about giving free compute" I have a mid-sized GPU cluster and was thinking about giving free compute isn't a direct solution, but it does point towards a trend of democratizing AI infrastructure and empowering more users to experiment and build models, further intensifying the need for comprehensive monitoring.
Ultimately, the hospital’s situation exemplifies a critical inflection point in the evolution of MLOps. The initial focus on model building and deployment is shifting towards a greater emphasis on governance, auditability, and continuous monitoring, particularly in regulated industries. The question now is whether existing MLOps platforms will evolve to meet these demands, or if we’ll see the emergence of specialized monitoring solutions that can bridge the gap between in-house and vendor-managed models. The need for a unified, flexible, and auditable MLOps solution is clear, but the path to achieving it remains a significant challenge – one that will shape the future of AI adoption in healthcare and beyond.
TL;DR: Hospital, fully on-prem OpenShift cluster. Multiple teams building prediction models, so we’re setting up a self-service platform with boundary policies. Evaluating ClearML vs OpenShift AI for the full MLOps lifecycle. Both look fine for development/deployment, but neither seems to give us production monitoring at the level we need (drift, bias, live dashboards per model). Extra twist: we also need to monitor models that run at our vendors, where all we get is an input/output data feed. Looking for real-world experience.
Our situation
We’re a hospital running an on-prem OpenShift cluster. No cloud, patient data stays inside the building.
We have multiple teams across the organisation working on prediction models, at quite different levels of maturity. So what we’re building is a self-service platform with boundary policies: teams get their own project/namespace, and can work independently — but within guardrails we define centrally (access control, resource limits, what can be deployed to production, what has to be logged and monitored). We don’t want to be the bottleneck for every team, but we also can’t have twelve teams each inventing their own way of putting a model into clinical use.
That means we’re looking for a full MLOps lifecycle platform on our on-prem cluster — data prep, notebooks, training, pipelines, model registry, serving, and monitoring — and we want to pick the right stack now.
We’re currently evaluating:
• Red Hat OpenShift AI (we already run OpenShift)
• ClearML (also self-hosted)
For development — notebooks, pipelines, training, model registry, serving — both look reasonable. That’s not really where our doubt is.
The actual problem: production monitoring
Our models make predictions that hospital staff act on. That means we fall under MDR (EU 2017/745) and the EU AI Act, so post-market monitoring and logging aren’t nice-to-haves — they’re legal requirements. What we need in production, live:
• Usage monitoring — who/what is calling the model, how often, is it actually being used or ignored
• Drift detection — data drift and prediction drift, per model
• Bias / fairness monitoring — and specifically subgroup performance (sensitivity/specificity/calibration per group), not just statistical parity, because in a clinical setting unequal miss rates are the actual harm
• Model-specific custom metrics — every clinical model has its own definition of “is this still working”
• Per-project dashboards — a model owner should be able to open one screen and see the state of their model, and in a self-service setup this has to work without central IT building it for them each time
• Alerting with a named owner — monitoring nobody responds to is worthless
• Immutable inference logging for audit/traceability
So I’ve been looking at running Evidently AI alongside it, self-hosted, computing metrics in a pipeline and pushing to Grafana. That seems like the pragmatic answer, but I’d like a sanity check before we commit.
The hard requirement: third-party vendor models
This is the part that seems to break most platform-native monitoring.
A growing share of our AI is bought from vendors and runs on their infrastructure. We don’t control the serving runtime, we can’t attach a sidecar, we can’t instrument anything. What we can do — and what we’re putting into procurement contracts — is require the vendor to deliver us the input/output data of every inference, which we then ingest and run our own monitoring pipeline on.
Legally the vendor is the manufacturer/provider and owns post-market surveillance, but as the deployer we still have our own obligations, and frankly we want independent evidence rather than just trusting their reporting.
At the same time we’re doing more and more in-house model development, which is exactly why we want one platform covering the full lifecycle rather than only a monitoring tool bolted on afterwards. Whatever we choose has to handle “model running on our own cluster” and “model running at a vendor” in one consistent way.
What I’m hoping to learn
Given all of the above, what would you say is the best solution for our environment, and does anyone have real-world advice on platforms from running something like this in a regulated environment?
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