MLOps Consulting Services
Crescent AI engineers MLOps for teams training and running their own models — feature pipelines, training runs, model registries, and drift monitoring — distinct from LLMOps (operating prompt- and retrieval-based applications) and from the broader shared-platform work most teams don't need on day one.
MLOps for Models You Actually Train
If you're fine-tuning or training models on your own data, the hard part isn't the training run, it's everything around it: getting features computed the same way twice, knowing which version is live, and catching drift before a customer does. That's the lifecycle we engineer.
What is MLOps?
MLOps is the discipline of engineering the lifecycle of models trained on your own data: feature pipelines, training runs, model registries, versioning, drift monitoring, and retraining, so a trained model keeps performing correctly in production instead of degrading silently.
Why this isn't just AI Platform Engineering
MLOps is the model-training-lifecycle layer: one model, one team, feature pipelines through retraining. AI Platform Engineering is the broader shared system — catalog, model gateway, golden paths, governance — that MLOps, LLMOps, and infrastructure all plug into once more than one team is building AI. Most engagements start with MLOps scoped to a single team's models; the shared platform is a separate decision, made after that first team is running cleanly.
Recognize the symptoms
When You Need MLOps
If two or more of these are already true, this isn't a tuning problem.
- Feature pipelines are duplicated or hand-built per model
- Nobody can say which model version is live without asking the person who trained it
- There's no automatic check for drift once a model ships
- Retraining is manual and undocumented, run only when someone remembers
What We Engineer
Five parts, engineered as one lifecycle, not five disconnected scripts.
Feature Pipelines
The pipelines that turn raw data into the exact inputs a model was trained on, computed the same way in training and in production.
Training & Experiment Tracking
Every training run recorded — data version, parameters, and results — so a model's lineage is a lookup, not a guess.
Model Registry & Versioning
A central record of every trained model version, what's approved, and what's actually running in production.
Drift & Performance Monitoring
Automatic checks for when input data shifts or accuracy degrades, so a model doesn't quietly get worse between reviews.
Automated Retraining Pipelines
A retraining workflow triggered on a schedule or by a drift alert, tested against the current model before it replaces it.
MLOps We've Implemented
Concrete builds, not a slide about best practices.
CI/CD for Model Training
Training runs kicked off automatically on new data or code changes, with results tracked and compared before anything ships.
Feature Store Implementation
One shared source of features computed consistently across training and serving, so models stop drifting apart from subtly different pipelines.
Model Registry & Approval Gates
A registry with a required check before a new model version can go live, not a folder of pickled files on someone's laptop.
A/B and Shadow Deployment for Models
New model versions tested against the current one on real traffic before fully replacing it, with a fast rollback if results don't hold up.
Automated Drift-Triggered Retraining
Retraining that kicks off when monitored drift crosses a threshold, not only when someone remembers to check.
What You Receive
From our experts.
Writing on model evaluation and production AI cost.
Related Engineering Services
Common questions.
Bring us the models nobody's tracking properly.
Whether it's one model with no registry and no drift monitoring, or a training pipeline held together by hand, we'll walk through where it stands before we recommend anything.
No hype · No forced roadmap · Just a clear view of what the system needs next