Affine
Data Science & Machine Learning

ML in Production.
Not Just in Notebooks.

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.

Data Science and Machine Learning in production
The Challenge

Most ML Projects Die in PowerPoint.
Ours Run in Production.

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.

Proof Points
15+
Years in Production ML
50+
Fortune 500 Clients
~94%
Client Retention Rate
Solutions

Four Capabilities. One Production-Ready ML Stack.

Each layer is independently deployable and proven in production - combine them for compounding returns.

01

Forecasting & Propensity Models

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.

~90% accuracy uplift
02

Deep Learning & Computer Vision

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.

~98% accuracy
03

MLOps & Model Lifecycle

CI/CD pipelines, A/B testing, model monitoring, and feature-store governance. Every model under version control, observable in production, deployable on demand.

~60% faster time-to-market
04

Reinforcement Learning & Optimization

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.

Adaptive decisioning at scale
Our Approach

The Engine Behind Production ML.

Four proven systems - each battle-tested in enterprise deployments - working in concert.

01

MLOps Framework

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.

Stable model ops
02

Azure ML Reference Architecture

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.

Azure-native ML at speed
03

Customer 360° Accelerator

Pre-built hyper-personalization stack - user journey analysis, pain-point detection, behavioral segmentation - operating across the full customer view.

Personalization at scale
04

Industry Use-Case Library

Pre-built ML solutions tested in CPG, Insurance, Retail, and Automotive. Production-grade starting points - not greenfield builds - for industry-specific outcomes.

Cross-industry deployments
Case Studies

Proof, Not Promises.

Automated Defect Detection for a CPG Femcare Line

Deep-learning vision pipeline replacing manual inspection across high-velocity production - error rates dropped, throughput climbed, operators redeployed to higher-value work.

Measured Impact
  • →~98% defect-detection accuracy
  • →~95% reduction in manual inspection effort
  • →~1,000 man-days reclaimed annually
CPG · Manufacturing Quality

Demand Forecasting & Inventory Optimization for an F&B Enterprise

Production ML stack streamlining product-portfolio decisions - SKU-level demand signal feeding inventory and replenishment workflows.

Measured Impact
  • →SKU × geography forecast accuracy lift across the portfolio
  • →Inventory carrying cost reduction at scale
  • →Replenishment shifted from weekly cycles to demand-driven
F&B · Demand Planning

Agent Performance Platform for an Insurance Enterprise

CRM-integrated KPI dashboard surfacing benchmarks, conversion patterns, and intervention triggers across the full agent network.

Measured Impact
  • →Measurable lift in agent conversion performance
  • →Faster identification of underperforming segments
  • →Real-time KPI visibility across the agent base
Insurance · Agent Performance
Start Now

Ready to put ML in production?

Talk to our experts and get a tailored roadmap for your business.