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Machine predictive models.

Custom machine learning models for forecasting, recommendation, classification, anomaly detection, and segmentation — built on your data, evaluated rigorously, and deployed to production with proper MLOps.

Overview

What it means in practice.

Most ML projects fail at deployment, not modeling. We focus on the unglamorous parts that make ML actually work in production: feature pipelines, model registries, monitoring, retraining schedules, and graceful degradation when models lose accuracy. The notebook-to-production gap is where we earn our keep.

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What we deliver

Capabilities & deliverables.

Every engagement gets shaped to fit, but these are the building blocks we rely on.

Predictive Models

Demand forecasting, churn prediction, lead scoring, and pricing optimization. Tabular and time-series modeling tuned for your domain.

Recommendation Systems

Collaborative filtering, content-based, and hybrid recommenders for e-commerce, content platforms, and B2B products.

Classification & Segmentation

Customer segmentation, risk scoring, fraud detection, and lead qualification. Models you can explain to your business stakeholders.

Anomaly Detection

Operations monitoring, fraud detection, and unusual-pattern flagging in transactional and behavioral data.

Feature Engineering

Feature stores, pipelines, and offline-online consistency. The unglamorous work that determines whether models actually ship.

MLOps Infrastructure

CI/CD for models, A/B testing, shadow deployments, and rollback procedures. Models treated like any other production service.

PyTorch scikit-learn XGBoost LightGBM MLflow Weights & Biases Feast Kubeflow

Why it works

The SD Technolabs approach.

Two decades of engineering practice, sharpened by the realities of production AI.

Notebook to production, completed

We handle the deployment, monitoring, and retraining loop most teams stop short of. ML that actually serves users.

Model interpretability

SHAP values, feature importance, and explanation interfaces for stakeholders who need to trust the model before adopting its outputs.

Honest about ROI

We model the expected business impact before training and verify it after deployment. ML projects that don't pay back get killed early.

Drift monitoring

Data drift, concept drift, and accuracy degradation tracked continuously. Retraining triggers based on signal, not calendar.