A model in a notebook changes nothing.
Forecasts, recommendations and models built on your real data, from demand forecasting to customer segmentation. Each one ships with the monitoring and retraining that keep it accurate, and an explanation the business can trust.
Model lifecycle
Azure
Accuracy in a notebook is not the same as value in production. We build the models that make it there.
Demand, revenue and risk forecasts with confidence intervals leadership can plan against.
Engines that lift conversion and retention, tuned to the metrics that matter to the business.
Classification, extraction and search over unstructured text, grounded in your own corpus, with audit trails.
Catch outliers and threats early, balancing recall against friction.
Features from governed data, models tracked from first experiment to production, and monitoring that catches drift before the numbers do. It runs in your own cloud account on AWS, Google Cloud or Azure.
In your cloud accountOne environment from data foundation to production: experiments tracked, models tested before release, and served where the decision is made.
A machine learning environment on Azure Data Lake and Databricks, from data foundation through production; forecasts 20% more accurate.
Read the case study
Start with the decision a model would change, then the data it needs.
From our case studies, each figure as the study states it:
InsuranceProducing the weekly risk report
Every model ships with the monitoring and retraining that keeps it useful, and the explainability that lets the business trust what it recommends.
Drift is watched in production, so performance does not erode after launch with nothing watching for it.
Retraining pipelines, not one-offs, so models stay accurate as the data moves.
Decisions you can defend. Predictions the business cannot explain go unused.
Predictive models, recommendation engines and NLP built on your real data, from customer segmentation to demand forecasting, deployed with monitoring, retraining pipelines and explainability from day one. Four kinds of work: predictive and forecasting models, recommendation and personalization, NLP and document intelligence, and anomaly and fraud detection.
Yes. We build demand, revenue and risk forecasts with confidence intervals leadership can plan against. Accuracy in a notebook is not the same as value in production, so we build the models that make it there, with the monitoring and retraining that keeps them useful.
Drift is watched in production, so performance does not erode after launch with nothing watching for it, and retraining pipelines, not one-offs, keep models accurate as the data moves. A model in a notebook changes nothing; every model ships with the monitoring and retraining that keeps it useful.
Yes. Every model ships with the explainability that lets the business trust what it recommends: decisions you can defend. Predictions the business cannot explain go unused.
From our case studies: MasterControl's churn model took 12 weeks from data to a scored dashboard, Tungsten's copilot went live after a 35-week build, and FedEx's machine learning environment ran 44 weeks, from data foundation through production, with four model families.
FedEx improved package volume forecast accuracy by 20% with a machine learning environment on Azure Data Lake and Databricks. MasterControl flags customer churn 90 days early with 85% accuracy, from a model on AWS SageMaker trained on 3 years of customer history. Both are Level 2 (L2) systems.