Where Does Your ML Team Sit on the MLOps Maturity Scale?

MLOps maturity is one of those topics that gets over-complicated by vendors trying to sell you their platform. The reality is simpler. There are roughly five levels, most teams are stuck at Level 1, and moving to Level 2 is where the biggest ROI is. Here is the framework we use to assess ML teams and help them improve.

Level 0: Manual Everything

This is where every ML team starts. A data scientist trains a model in a Jupyter notebook, exports the weights, and someone (often the same person) SSHs into a server and copies the file over. There is no version tracking for models or data. If the model breaks, you re-run the notebook and hope the training data has not changed too much since last time.

Warning signs: model files shared via Slack or email, no record of which model version is in production, "it works on my machine" as a deployment strategy.

Level 1: Scripted

Training is wrapped in Python scripts instead of notebooks. There might be a shell script that deploys the model to a server. Someone set up basic logging so you can see if the prediction endpoint is returning 500 errors. But there is no CI/CD for models, no automated testing of model quality, and no systematic way to compare model versions.

Most teams we encounter are here. It works until you need to retrain frequently, manage multiple models, or debug why predictions degraded last Tuesday. Getting from Level 1 to Level 2 is the single highest-ROI investment an ML team can make.

Level 2: Automated

This is where things start to feel professional. Training pipelines run automatically on a schedule or when new data arrives. Models are stored in a model registry (MLflow, Weights & Biases, SageMaker Model Registry) with version numbers, metrics, and metadata. There is a CI/CD pipeline that tests model quality before deployment: if accuracy drops below a threshold, the deployment is blocked.

Key tools at this level: MLflow or W&B for experiment tracking, Airflow or Dagster for pipeline orchestration, a model registry with approval gates, and automated tests that compare new model performance against the current production model.

Level 3: Monitored

Level 3 adds production monitoring. You track not just whether the model is serving requests, but whether the predictions are any good. Data drift detection alerts you when the input distribution changes. Prediction drift detection catches the model's output distribution shifting. A/B testing infrastructure lets you roll out new models to a percentage of traffic and compare performance.

This is where most ML teams should aim to be. You catch problems before users notice them, you can quantify the impact of model changes, and you have the data to make informed decisions about when to retrain.

Key tools: Evidently or NannyML for drift detection, a feature store (Feast) for consistent feature computation, an A/B testing framework, and dashboards showing model performance metrics alongside business metrics.

Level 4: Optimized

At the highest level, models retrain themselves when drift is detected. The system automatically evaluates the new model, compares it against the current one, and promotes it if it performs better. Feature engineering is automated with feature stores. The team spends most of its time on new model development rather than maintaining existing ones.

Very few teams reach Level 4, and honestly, most do not need to. It makes sense when you have dozens of models in production, when the data changes rapidly, and when the cost of a stale model is high (fraud detection, real-time pricing, recommendation engines).

How to Move Up

From 0 to 1: Convert notebooks to scripts. Add basic logging. Write a deployment script. This takes a week.

From 1 to 2: Set up MLflow for experiment tracking. Build a training pipeline in Airflow or Dagster. Add automated quality checks before deployment. This takes 2-4 weeks and is the most impactful transition.

From 2 to 3: Add data drift monitoring (Evidently is a good starting point). Build prediction quality dashboards. Set up A/B testing for model rollouts. This takes 4-8 weeks.

From 3 to 4: Automate retraining triggers based on drift alerts. Build an auto-evaluation pipeline. Implement a feature store. This takes 2-3 months and requires significant engineering investment.

Common Pitfalls

Do not try to jump from Level 0 to Level 3. We have seen teams buy an expensive MLOps platform before they have a single automated pipeline. The platform sits unused because nobody has the foundational workflows it expects. Move one level at a time, and make sure each level is solid before investing in the next.

Also, do not over-invest in tooling for a single model. If you have one model in production that gets retrained quarterly, Level 2 is plenty. Save the Level 3 and 4 investments for teams managing multiple models with frequent updates.