AI & Machine Learning
MLOps is the engineering discipline that takes machine learning models from notebook prototypes to production systems. It covers CI/CD for ML, model registries, feature stores, monitoring, retraining, and the operational practices that keep AI working after launch.
At a glance
Definition
MLOps is what separates an AI proof-of-concept from a production system. It's the discipline of operating machine learning at the same standard you'd apply to any other production software: versioning, automated testing, monitoring, rollback, and incident response, but adapted to the unique failure modes of statistical models.
§01
A mature MLOps practice spans seven layers: experiment tracking (so the model in production is reproducible), feature stores (so training and inference use the same transformations), CI/CD pipelines for both code and models, a model registry (with provenance metadata), serving infrastructure (batch and real-time), monitoring (latency, accuracy, drift, cost), and retraining loops triggered by metric thresholds.
§02
Data science teams optimize for model accuracy on a fixed dataset. MLOps optimizes for the model's behavior in production, where data drifts, infrastructure fails, and user behavior changes. Teams that skip MLOps ship the model, declare victory, and quietly degrade for months until someone notices the metrics have flatlined. Adding MLOps after launch is two to five times more expensive than designing it in.
§03
Vendors (Sagemaker, Vertex AI, Databricks, Weights & Biases, MLflow) cover most of the layers, but stitching them into a cohesive platform is engineering work. Self-hosted open-source stacks (MLflow + Ray + Seldon + Prometheus) give more control at higher operational cost. The right answer depends on team size, regulatory requirements, and existing cloud commitments. There's no universal best.
Approach
We design the MLOps stack before we train the first model. The reference architecture is opinionated: model registry as source of truth, feature store at the boundary, evaluation harness on every commit, drift monitoring per feature, automated rollback on metric regression, but tooling is chosen for the client's existing cloud and team. Our Managed Services engagement model covers ongoing operations against a published SLA, with on-call coverage and quarterly model refresh cycles.