MLOps Consulting
MLOps consulting: model deployment, drift and quality monitoring, model observability, and retraining. Keep your ML and AI models accurate and governed after launch.
What this service covers
- MLOps consulting
- MLOps services
- model operations consulting
- machine learning operations
- model observability
- model monitoring
- ML model deployment
- model drift
Frequently asked questions
What is MLOps and why does it matter?
MLOps is the operations layer that keeps a machine learning or AI model working after launch: automated deployment, monitoring for drift and quality, retraining triggers, and rollback. It matters because a model that is accurate on launch day quietly degrades as the data changes, and MLOps is what catches that before it reaches a customer or a decision.
What is the difference between data observability and model observability?
Data observability watches the pipelines feeding the model: freshness, volume, schema, and distribution. Model observability watches what the model does: accuracy, hallucination rate, drift, latency, cost, and whether each answer can be traced. You need both, because a clean pipeline can still feed a model that degrades, and a good model on stale data produces confident nonsense.
Do you cover LLMs and agents, or only traditional ML models?
Both. Classic ML models need drift and accuracy monitoring; LLMs and agents also need hallucination rates, output-quality benchmarks, token cost, and, for agents, which actions they take and how often a human overrides them. We instrument whichever you run in production.
How do you catch a model that is drifting?
We baseline the model's inputs and outputs, then monitor both continuously against that baseline. When the input distribution shifts or output quality slides past a threshold, the named owner gets an alert, and where it makes sense a retraining pipeline triggers. The point is to notice before a customer or a decision does.
Can you operate our MLOps, or only set it up?
Either. We build the deployment, monitoring, and retraining plumbing, and we can run it on a retainer with a named engineer, handling alerts, retraining, and model updates as an ongoing service. Many clients start with the build and move to managed operations once it is in place.
How long to stand up MLOps on an existing model?
For a single model already in production, a scoped MLOps setup with deployment, monitoring, and rollback typically ships in 6 to 10 weeks. A broader program across several models takes longer, but we start with the model whose failure would hurt most.
Request the 30-day Analytics Truth Audit to scope this engagement for your environment.