Machine Learning Solutions

Forecast, classify, and personalize with models that are explainable, monitored, and built for real workloads.

Right-fit Models
Classical to deep learning—chosen for outcomes, not hype.
Governed by Design
Versioned data, approvals, rollbacks, and audit trails.
Built to Perform
Latency targets, cost controls, and drift monitoring.
Model development and evaluation

Model the Problem, Not the Buzz

We begin with the business signal and data reality—then select methods that maximize clarity and ROI. That means tried-and-true approaches where they win, and modern architectures where they matter.

  • Forecasting (demand, capacity, time-series)
  • Classification & ranking (risk, propensity, churn)
  • Recommendation & personalization (next best action)

From Notebook to Service

Reproducible builds, containerized deployments, and clear handoffs ensure the path from experiment to production is reliable—and reversible.

  • Versioned datasets, code, and artifacts
  • CI/CD with checks, approvals, and rollbacks
  • API or batch endpoints with SLO-aligned configs
Operational Patterns
Blue/green and canary rollouts; shadow deployments for safe validation under real traffic.
Monitoring & Drift
Live dashboards for performance and fairness; alerts when data shifts or quality drops.
Governance
Traceable lineage and sign-offs—so you can explain changes and restore prior versions.
Interoperability
.NET services, Python tooling, and data platforms play well together—we integrate, not replace.
Security by Default
Secrets management, scoped permissions, and environment isolation to protect sensitive work.

Make It Measurable

Models that win in production are monitored against the goals that matter—accuracy when it counts, speed when it’s required, and cost when it scales.

  • Success metrics aligned to outcomes
  • Real-time and batch evaluation hooks
  • Budget and latency guardrails

When to Use What

Scenario Often Best Why
Demand forecasting with clear historical patterns Time-series (ARIMA/Prophet) or LightGBM Strong baselines, interpretable, competitive with careful features.
Real-time classification (risk, fraud, eligibility) Gradient boosting / calibrated linear Fast, robust under constraints; easier to harden & audit.
Personalization / ranking Matrix factorization + GBM rerank Great balance of performance, speed, and explainability.
Complex image or text signals Fine-tuned deep models Leverage pretrained backbones; deploy with guardrails.

Ship models that move the needle

We’ll scope a solution that’s measurable, governed, and production-ready.

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