Your AI model went live. Now it's slowly getting worse and nobody knows.

MLOps, machine learning operations, is the set of practices and infrastructure that keeps AI models performing reliably in production over time. Most AI projects focus heavily on model development and treat deployment as the finish line. In practice, deployment is where the ongoing work begins. Real-world data changes constantly: customer behaviour shifts, product catalogues expand, fraud patterns evolve, sensor environments change. A model trained on historical data gradually becomes a model trained on the wrong data as the world it was built to understand diverges from the world it is asked to predict. MLOps puts monitoring and maintenance infrastructure in place before this becomes a problem. Model monitoring tracks key metrics continuously. Drift detection identifies when incoming data no longer matches the training distribution. Automated retraining pipelines rebuild and validate the model when drift thresholds are crossed. Experiment tracking ensures every model version is reproducible. These systems turn AI from a one-time build into a maintained capability.

Data drift occurs when the statistical properties of the input data your model receives in production diverge from the data it was trained on. There are two types that matter. Feature drift means the inputs themselves are changing, your customer demographics are shifting, transaction volumes are moving, or the distribution of product categories in your catalogue has changed. Concept drift means the relationship between inputs and correct outputs has changed, fraud tactics have evolved, customer preferences have shifted, or the macro environment has changed the meaning of the signals your model uses. Feature drift is detectable statistically by comparing incoming data distributions to training data. Concept drift is harder to detect because it requires ground truth labels from production, which often arrive with a delay. Our monitoring design accounts for both. For each use case, we define the appropriate drift metrics, detection thresholds, and alert logic based on how quickly drift translates to business impact in your specific context.

Automated retraining pipelines work in three stages: trigger, retrain, and validate. The trigger is a drift threshold, when model performance metrics or data distribution metrics cross a defined boundary, the pipeline fires. Retraining pulls fresh labelled data from your data pipeline, combined with historical training data, and runs the model training job in a reproducible environment. Validation runs the retrained model against a held-out evaluation set and a set of business-logic tests before it is promoted to production. If the retrained model fails validation, it does not deploy and the team is alerted. If it passes, it deploys through your standard deployment pipeline and the previous model version is retained for rollback. The trigger thresholds and validation criteria are defined during scoping based on how sensitive your use case is to model degradation. Some contexts warrant retraining when drift crosses a statistical threshold. Others require business metric confirmation. We design the pipeline around the tolerance for false positives and false negatives in your specific application.

Application monitoring watches whether the system is up and responding: response times, error rates, infrastructure health. MLOps monitoring watches whether the outputs are correct: whether the model's predictions are still accurate, whether the data flowing through the system still looks like it should, and whether business metrics tied to AI output are tracking as expected. Both matter, but they catch different failure modes. Application monitoring tells you the API is returning 200. MLOps monitoring tells you the answers it is returning are wrong. For AI systems where accuracy directly affects revenue, fraud exposure, or customer experience, monitoring only the application layer is a significant gap. We integrate with your existing application monitoring infrastructure and add the model-specific monitoring layer on top.

MLOps engagements vary in scope. A focused monitoring and drift detection layer for a single production model typically runs between $15,000 and $40,000 depending on the number of features monitored, the complexity of alert routing, and the monitoring tooling selected. A full MLOps platform build including experiment tracking, model registry, automated retraining pipelines, and feature store integration starts around $50,000 and scales with the number of models, data sources, and cloud environment complexity. All engagements are scoped at a fixed price after a 1-week discovery phase. You receive a written quote before any development starts.

The stack depends on your existing infrastructure and team. For experiment tracking and model registry, we work with MLflow and Weights and Biases. For pipeline orchestration, we use Apache Airflow, Prefect, and AWS SageMaker Pipelines. For model monitoring, we deploy Evidently AI or Arize, or custom Prometheus-based monitoring exported to Grafana. Data versioning is handled with DVC. Infrastructure is defined as code using Terraform. We work across AWS SageMaker, Azure ML, and Google Vertex AI. We do not have a preferred vendor lock-in, the right tool for your team and infrastructure is the right tool for the job.