MLOps
Services

MLOps is the set of engineering practices that move machine learning models from experimentation into reliable production and keep them reliable afterwards.

It covers training pipelines, experiment tracking, model registries, feature management, deployment, monitoring and retraining, applied consistently across every model.

MLOps services design, build and operate the lifecycle infrastructure behind production machine learning: CI/CD for models, registries, feature stores, release paths, monitoring and retraining workflows.

The goal is that any model can be rebuilt, released, monitored and rolled back through a repeatable process rather than individual effort.

ML development builds the model itself: framing the problem, preparing data, choosing an approach and training something that performs well.

MLOps begins once that model needs to be released, versioned, monitored and retrained in production, often alongside other models.

ML CI/CD is continuous integration and delivery for machine learning. As well as testing code, it tests training data and model behaviour, because a model can change while the code stays the same.

It adds promotion gates, packaging of one immutable model artifact, gradual release strategies and a rollback path to the previous model.

A model registry is the system of record for model versions. Each version links to the run that produced it and carries metadata, lineage, lifecycle state and approval history.

It also records where each version is serving, which makes deployment decisions auditable and rollback a matter of looking up the previous version.

Experiment tracking records every training run with its parameters, metrics, code commit, dataset versions and output artifacts.

It lets teams compare candidates fairly and reproduce any result later, and it supplies the evidence a registry needs when a candidate is promoted.

A feature store defines, computes, stores and serves machine learning features consistently. It keeps historical values for training and current values for live predictions, both produced from the same definitions.

It prevents training-serving skew and supports point-in-time correct training data. Not every system needs one; it pays off when features are shared or served online.

Models are deployed for batch inference on a schedule or behind online endpoints for real-time predictions.

Releases are usually gradual: shadow deployment against live traffic, a canary on a small share of requests, then a full switch, with the previous model kept available for rollback.

Continuous training retrains models through an automated pipeline when a schedule, new labelled data or a monitoring signal calls for it.

Retrained candidates should pass the same validation gates as any new model. For high-impact models, promotion usually keeps a human approval step.

We track input drift, prediction drift and performance against the approved baseline, with thresholds and alerts routed to a named owner.

System telemetry such as latency, errors and dependency health is monitored alongside, so a quality change can be traced to its cause.

MLOps focuses on the lifecycle of trained models and the data behind them.

LLMOps extends that lifecycle to prompts, retrieval indexes, model routing, evaluation of open-ended output and dependencies on external model providers.

Not to the same degree. A one-off analysis or a model scored occasionally by hand needs little lifecycle tooling.

Models that make recurring decisions, serve live traffic, retrain on new data or must be explained later need repeatable deployment, versioning and monitoring.