From Six Weeks to Four Days Per Model
MLOps consulting that industrializes the model lifecycle — feature stores, CI/CD for models, drift-triggered retraining, and monitoring — on Databricks, Azure ML, Vertex AI, and Kubeflow.
Why MLOps & ModelOps Programs Stall
The first model is easy; the fortieth is the problem. Without MLOps, every model is a bespoke deployment — data scientists babysitting pipelines, six-week release cycles, silent drift degrading decisions in production, and no one able to say which model version made which call. The backlog grows and the platform team burns out.
The Apptad Approach to MLOps & ModelOps
We build the factory: feature stores that make training data reusable, CI/CD that treats models like software, automated retraining triggered by measured drift, and monitoring that catches degradation before the business does. Your data scientists go back to science; the platform ships.
Feature stores as the foundation
Co-designed with your domain teams — reusable, governed features end the copy-paste-pipeline era.
CI/CD for models
Versioned, tested, promoted like software. One banking client went from 6 weeks to under 4 days per new fraud model.
Drift-triggered retraining
Automated retraining pipelines fired by measured input/performance drift — not by the quarterly calendar.
Sub-100ms serving where it counts
Real-time scoring architectures proven at 50M+ transactions/day in production fraud detection.
MLOps & ModelOps in Production
Top-10 Bank Saves $28M/Year with Real-Time Fraud Detection on Databricks
Replaced a brittle rules-based fraud system with a real-time ML platform on Databricks + Snowpark, scoring 50M+ transactions daily.
Read the case study
Global Manufacturer Cuts Unplanned Downtime 31% with Predictive Maintenance
Industrial IoT + ML on Azure delivered predictive maintenance across 14 plants — saving $19M in lost production hours.
Read the case studyMLOps & ModelOps, Answered Straight
When does a company actually need MLOps?
At roughly the third production model, or the first one whose failure costs real money. Before that, discipline beats platform. After that, every month without a feature store and model CI/CD adds bespoke deployments you'll eventually re-platform anyway.
Databricks, Azure ML, or Vertex — does the platform matter?
Less than the operating model. All three run the full lifecycle competently; the right pick follows your data gravity and cloud commitments. What matters is what sits on top: feature governance, evaluation gates, drift response. We've shipped production MLOps on all three.
How do you handle model monitoring and drift?
Three layers: input drift (is the data changing?), performance drift (are outcomes degrading?), and business-metric tracking (does anyone care?). Alerts route to owners with automated retraining as the default response and human review on the exceptions.
Part of our AI / ML practice.
Have a MLOps & ModelOps Question? Ask the People Who Ship It
Our AI / ML practice is led hands-on. Connect on LinkedIn or send a note through the form — a practitioner (not a sales rep) replies within one business day.
