Machine Learning Engineering
& Predictive Systems
From high-concurrency real-time recommendation engines to predictive forecasting and anomaly detection models, we design, train, and operationalize resilient machine learning architectures that turn raw enterprise telemetry into high-conviction decisions.
What We Delivered:
Real-Time Predictive Fraud & Risk Scoring Engine
Client: FinTech Challenger Bank (1.8M Active Accounts) · Financial Technology & Neo-Banking · 8-Week Production Delivery
The Core Bottleneck
Rules-based fraud detection was flagging legitimate customer transactions (false positive rate of 6.8%), leading to angry churn while missing novel fraud patterns.
The ModelScale Architecture
Trained gradient-boosted trees and graph neural networks on historical transaction graphs, deploying them on an automated MLOps pipeline with online feature stores.
Key Production Components
- Feast feature store syncing batch historical data with real-time Redis streaming features
- ONNX Runtime inference server achieving sub-18ms p99 execution latency
- Automated drift detection triggering weekly retraining pipelines in Kubeflow
Production Metrics
$2.3M saved in annual unauthorized chargebacks
Drastically improved card acceptance rates
Completed before card terminal payment timeout
Scope of Delivery & Architecture
Predictive Modeling & Forecasting
Developing time-series forecasting, churn prediction, and dynamic demand estimation models tuned to your historical business data.
Personalized Recommendation Engines
Collaborative filtering, two-tower neural recommenders, and contextual bandit systems that drive user engagement and conversion.
Enterprise MLOps & Continuous Retraining
Building automated ML pipelines from data validation and feature engineering to experiment tracking and canary deployments.
Feature Store & Low-Latency Serving
Architecting centralized feature repositories that bridge offline model training with real-time online inference with sub-15ms lookups.
Modern, Scalable Technology Foundation
- PyTorch
- XGBoost
- LightGBM
- Scikit-Learn
- CatBoost
- MLflow
- Kubeflow
- DVC
- Weights & Biases
- Airflow
- Feast
- Redis Cluster
- ONNX Runtime
- TorchScript
- Snowflake
- BigQuery
- Apache Spark
- DuckDB
How We Mobilize and Deliver
Phase 1: Exploratory Data Analysis & Features
Data hygiene, leakage auditing, baseline modeling, and metric definitions.
Phase 2: Model Architecture & Optimization
Hyperparameter tuning, cross-validation, ONNX conversion, and offline evaluation.
Phase 3: MLOps Pipeline & Production Serving
Feature store sync, automated retraining triggers, shadow deployment, and API integration.
Transparent Engagement Tiers
Clear milestones, guaranteed IP ownership, and zero lock-in.
Predictive Model Sprint
Deliver an accurate, production-ready predictive model for a specific business KPI.
- Full feature engineering and model training
- ONNX conversion and sub-50ms API endpoint
- Offline evaluation and validation benchmarks
- Deployment to your cloud environment
End-to-End MLOps Platform
PopularComplete enterprise machine learning system with feature store, automated retraining, and monitoring.
- Full MLOps pipeline on Kubernetes
- Centralized feature store with real-time Redis sync
- Data drift and accuracy degradation alerts
- Dedicated ML Engineer & MLOps Specialist
- Comprehensive documentation and team handoff
Dedicated MLOps Pod
Ongoing model maintenance, retraining pipelines, feature additions, and infrastructure scaling.
- 1 Senior ML Scientist, 1 MLOps Engineer, 1 Backend Engineer
- Continuous model drift monitoring and retraining
- SLA guaranteed response times
Frequently Asked Questions
How do you detect when a machine learning model is losing accuracy in production?
We set up real-time telemetry monitoring data drift (Kolmogorov-Smirnov tests) and concept drift. When input distributions deviate beyond thresholds, alerts fire and automated retraining pipelines trigger.
Can your models operate under strict latency limits (< 20ms)?
Yes. We compile trained PyTorch models to ONNX and TensorRT formats, caching frequent features in Redis for microsecond-level retrieval.
How do you explain model decisions to non-technical stakeholders and auditors?
We integrate SHAP (Shapley Additive exPlanations) and LIME into custom dashboards, detailing exactly which features drove each individual prediction.
Your next release runs on our stack.
Book a 30-minute strategy call. We'll audit one workflow, benchmark it against our best-of, and hand you a written roadmap — free.