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MACHINE LEARNING

ML models that ship to production , not just notebooks.

Most ML projects die in the notebook. We take them to production. Dezvo builds classification, recommendation, forecasting, computer vision, and NLP systems — with proper data pipelines, model monitoring, and retraining workflows. For when LLMs aren't the right tool.

See Our Work
What we ship
  • Classification & ranking models
  • Recommendation engines
  • Time-series forecasting
  • Computer vision pipelines
  • Production MLOps stack
ML USE CASES

Where custom ML beats generic LLMs.

Classification

Spam detection, fraud scoring, lead qualification, image categorisation. Faster and cheaper than LLM calls at scale.

Recommendation

Product, content, dealer-to-buyer matching. Collaborative filtering, content-based, hybrid.

Forecasting

Demand, inventory, revenue. ARIMA, Prophet, deep learning — we pick what fits the data.

Computer Vision

Defect detection on manufacturing lines, tile pattern matching, OCR, image moderation.

FAQ

Common questions, answered.

If your question isn't here, message us — usually same-day reply.

When you have lots of structured data, need low latency, or need to run at millions of predictions per day. LLMs are powerful but slow and expensive. A 50ms scikit-learn model beats a 2-second LLM call for high-volume classification.

Python — scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM. Data via Pandas / Polars. Serving via FastAPI or BentoML. Tracking with MLflow or Weights & Biases.

Yes — ETL pipelines, feature stores (Feast), data validation (Great Expectations). The data work is usually 70% of an ML project.

Model versioning, drift monitoring, retraining workflows, A/B testing models in production. Built right, not bolted on later.
RELATED SERVICES

Bundle the services that work together.

AI Integration

When the right answer is LLMs instead of custom ML.

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API Development

The serving layer your ML models sit behind.

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Python Development

Backend Python work that complements ML.

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