# MLOps Maturity Assessment: Is Your Production AI Actually Operable? > Source: https://netray.co/tools/mlops-maturity-assessment > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer Score your model versioning, deployment automation, monitoring, drift detection, and rollback practices to find the specific MLOps gaps putting production models at risk. ## Category ai-automation ## Tool **Kind:** assessment **Questions:** #### How are model versions tracked and reproduced? **Options:** ##### Full versioning of model, data, and code with one-command reproducibility **Score:** 3 ##### Model versions tracked, data and code versioning inconsistent **Score:** 2 ##### Informal notes or filenames track versions **Score:** 1 ##### No version tracking **Score:** 0 #### How are models deployed to production? **Options:** ##### Automated CI/CD pipeline with staged rollout and automated tests **Score:** 3 ##### Semi-automated deployment with manual approval gates **Score:** 2 ##### Manual deployment by an engineer each time **Score:** 1 ##### No defined deployment process **Score:** 0 #### How is model performance monitored after deployment? **Options:** ##### Automated monitoring for accuracy, latency, and cost with alerting **Score:** 3 ##### Dashboards exist but are checked manually **Score:** 2 ##### We rely on user complaints to know something broke **Score:** 1 ##### No production monitoring **Score:** 0 #### Do you detect data or concept drift? **Options:** ##### Automated drift detection triggers a review or retraining process **Score:** 3 ##### We periodically check for drift manually **Score:** 2 ##### We have discussed it but not implemented it **Score:** 1 ##### No drift detection **Score:** 0 #### How is retraining or model refresh handled? **Options:** ##### A scheduled or trigger-based retraining pipeline exists **Score:** 3 ##### Retraining happens manually when someone notices degradation **Score:** 2 ##### Retraining has never happened since initial launch **Score:** 1 ##### No retraining process defined **Score:** 0 #### Can you roll back a bad model deployment quickly? **Options:** ##### Automated rollback to a known-good version within minutes **Score:** 3 ##### A manual rollback procedure that has been tested **Score:** 2 ##### A rollback is theoretically possible but untested **Score:** 1 ##### No rollback plan **Score:** 0 #### Is there a feature store or equivalent for consistency between training and serving? **Options:** ##### A managed feature store or equivalent ensures training and serving consistency **Score:** 3 ##### Some shared data pipelines exist, not formally a feature store **Score:** 2 ##### Training and serving pull from different, loosely synced sources **Score:** 1 ##### No consistency mechanism at all **Score:** 0 #### How is access to models and data governed? **Options:** ##### Role-based access control with full audit logging across the pipeline **Score:** 3 ##### Basic access control, limited audit trail **Score:** 2 ##### Access is informally managed by whoever set it up **Score:** 1 ##### No access governance **Score:** 0 **Bands:** #### Ad hoc: MLOps practice does not exist yet **Min:** 0 **Max:** 24 **Verdict:** Models are being deployed and maintained by hand, which works for a single pilot and breaks the moment you have more than one model in production. Every deployment currently depends on institutional memory rather than a repeatable process, which is a fragile place to run anything customer-facing. **Recommendations:** - Start with the two highest-leverage practices: basic monitoring and a tested rollback procedure. - Version models and the data used to train them before adding any new capability. - Write down the current manual deployment process as a first step toward automating it. - Treat the next model launch as the forcing function to build minimal CI/CD. #### Early practice: pieces exist, not a system **Min:** 25 **Max:** 49 **Verdict:** You have individual MLOps components but they are not connected into a repeatable pipeline. This is a normal state at one or two models in production and becomes a real liability once a third or fourth model is added. **Recommendations:** - Connect existing monitoring and versioning into a single deployment pipeline. - Add automated drift detection before your next model refresh cycle. - Formalize the rollback procedure and actually test it, not just document it. - Assign a named owner for MLOps practice, even if it is a part-time responsibility. #### Developing: a real pipeline with known gaps **Min:** 50 **Max:** 74 **Verdict:** You have a working MLOps pipeline that would survive most day-to-day incidents. The remaining gaps are usually around drift detection and feature consistency, which tend to surface only as the number of models and users increases. **Recommendations:** - Close the feature store gap before adding a third or fourth model to production. - Automate drift detection rather than relying on periodic manual checks. - Extend audit logging to cover the full pipeline, not just the serving layer. - Load-test your rollback procedure under realistic failure conditions rather than assuming it works. #### Mature: production-grade MLOps **Min:** 75 **Max:** 100 **Verdict:** Your pipeline can support multiple models in production with confidence, automated safety nets, and clear accountability. The focus now shifts from building the practice to keeping it efficient as the model portfolio grows. **Recommendations:** - Track pipeline cost and cycle time so growth does not silently erode the efficiency you have built. - Extend the practice to cover agentic and multi-model systems, which stress monitoring differently than single models. - Periodically audit access controls as team membership changes over time. - Use this maturity as leverage to take on more ambitious, higher-risk AI use cases. ## Content **Intro:** This free MLOps maturity assessment scores your production AI operations across eight dimensions, from model versioning and deployment automation to drift detection and access governance, and it is built for platform engineers and IT directors who need to know whether their AI systems can be operated reliably, not just demoed successfully. Answer eight questions about how models are actually versioned, deployed, monitored, and retrained today, and get a maturity band with prioritized next steps. Most organizations discover their MLOps gap the hard way, when a second or third model reveals that the first one's manual process never actually scaled. **Sections:** #### Why MLOps maturity matters more than model quality **Body:** A highly accurate model with no monitoring, no rollback plan, and no drift detection is a liability wearing a good demo. Models degrade silently as the real world drifts from the data they were trained or tuned against, and without monitoring, that degradation is invisible until users notice, at which point trust in the system has already been damaged. The gap between a working pilot and an operable production system is almost entirely MLOps practice, not model architecture. **Bullets:** - A model that cannot be rolled back quickly turns every deployment into a high-stakes event instead of a routine one. - Drift detection is the difference between catching degradation in days versus discovering it from a customer complaint months later. - Feature store or pipeline consistency gaps cause the classic training-serving skew, where a model performs differently in production than it did in evaluation. - Access governance gaps are invisible until an audit or incident forces the question of who could have touched the model or its data. #### The practical order to build MLOps maturity in **Body:** Do not try to build all eight dimensions simultaneously. Start with monitoring and a tested rollback procedure, since these two practices catch and contain the most common production incidents with the least engineering investment. Add automated deployment next, since manual deployment is where human error most often introduces production incidents. Drift detection, feature store consistency, and full audit governance are real investments worth making, but they pay off more as the number of models in production grows, so sequence them after the basics are solid rather than before. #### How MLOps maturity interacts with regulated environments **Body:** For manufacturers operating under ITAR or CMMC, access governance and audit logging are not optional maturity nice-to-haves, they are compliance requirements that an assessor will specifically test. A model deployment pipeline with no audit trail of who deployed what, when, and against which data is a finding waiting to happen. Build these dimensions early if you operate in a regulated environment, even if monitoring and drift detection are technically higher priority in an unregulated context. #### How Netray builds MLOps practice for manufacturers **Body:** Netray builds production-grade MLOps as a default part of every deployment, not an optional add-on scoped separately after launch. That means versioned models, automated deployment with staged rollout, monitoring wired to your existing alerting, and audit logging that satisfies CMMC and ITAR evidence requirements from day one. For customers running models on-prem, we also handle the GPU cluster monitoring layer that sits underneath the model-level MLOps practice. **Cta:** Get an MLOps gap assessment and a phased plan to close the highest-risk practices first. **Faq:** #### What is the single highest-priority MLOps gap to close first? **Answer:** Monitoring, closely followed by a tested rollback procedure. Without monitoring, you have no way to know a model has degraded until a user tells you, and without a tested rollback, fixing a bad deployment means an emergency engineering effort instead of a routine, low-stress operation. Both are achievable in weeks, not months, and they catch the majority of real production incidents on their own. #### Do we need a feature store for a single production model? **Answer:** Usually not yet. Feature stores solve consistency problems that mainly emerge once you have multiple models sharing data sources, or once training and serving pipelines have drifted apart over time. For a single model, disciplined manual consistency checks between training and serving data are often sufficient. Revisit this once a second or third model is in the pipeline. #### How is MLOps maturity different for LLM-based systems versus traditional ML? **Answer:** The core practices, versioning, deployment automation, monitoring, and rollback, apply equally to both. LLM systems add evaluation-specific monitoring, such as hallucination rate and groundedness scoring, that traditional ML monitoring dashboards were not built for, and drift often shows up as changing user query patterns rather than classic statistical feature drift. Budget for LLM-specific evaluation tooling in addition to standard MLOps infrastructure. #### Can a small team realistically reach high MLOps maturity? **Answer:** Yes, though it usually means adopting managed tooling rather than building every layer in-house. Small teams that reach the mature band typically use off-the-shelf platforms for versioning, deployment, and monitoring rather than custom infrastructure, reserving their engineering time for the model and evaluation work that is actually differentiated. Maturity is about having the practice in place, not about how much of it you built yourself. ## Search terms MLOps maturity assessment, MLOps readiness checklist, model deployment maturity, AI monitoring maturity, model drift detection maturity, MLOps best practices assessment, production ML maturity model --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json