Production ML is machine learning that runs as a reliable, monitored system in the business, not a one-off model in a notebook. Affine builds production ML - forecasting, propensity, computer vision, MLOps - engineered for accuracy at enterprise scale, not promising prototypes.
Models that hit 90%+ accuracy in a notebook routinely fail to ship - broken pipelines, governance gaps, monitoring debt. Affine engineers the production-grade infrastructure around every model so ML stays live, observable, and accountable to the business.
Each layer is independently deployable and proven in production - combine them for compounding returns.
Tailored ML models for demand forecasting, customer propensity, anomaly detection, and dynamic pricing. Deployed at SKU and customer granularity in production - measurable lift on the KPIs that matter.
Production vision pipelines for defect detection, facial recognition, traffic monitoring, and retail behavior analytics. NLP and speech models for the unstructured side - extracting decision-grade signal from raw input.
CI/CD pipelines, A/B testing, model monitoring, and feature-store governance. Every model under version control, observable in production, deployable on demand.
RL strategies for continuous decision refinement and scenario optimization for high-stakes planning. Systems that learn and adapt while business strategies stay resilient under uncertainty.
Four proven systems - each battle-tested in enterprise deployments - working in concert.
End-to-end model management lifecycle - ingestion, training, validation, deployment, monitoring. A stable foundation for ML at enterprise scale, aligned to business goals not lab metrics.
Production patterns for Azure-native ML - Model-as-a-Service, Feature Store, DevOps pipelines. Reduce time-to-market on Azure deployments without rebuilding from scratch.
Pre-built hyper-personalization stack - user journey analysis, pain-point detection, behavioral segmentation - operating across the full customer view.
Pre-built ML solutions tested in CPG, Insurance, Retail, and Automotive. Production-grade starting points - not greenfield builds - for industry-specific outcomes.
Deep-learning vision pipeline replacing manual inspection across high-velocity production - error rates dropped, throughput climbed, operators redeployed to higher-value work.
Production ML stack streamlining product-portfolio decisions - SKU-level demand signal feeding inventory and replenishment workflows.
CRM-integrated KPI dashboard surfacing benchmarks, conversion patterns, and intervention triggers across the full agent network.
