AI & data

Data science services

Machine learning models to forecast demand, detect anomalies or recommend the right product. We measure their accuracy on your own data before going live.

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Overview

Data science is at the heart of innovation. We combine statistical expertise, programming skills, and knowledge of your domain to extract valuable insights from your data. From classification to prediction, we develop end-to-end data science solutions that are robust, scalable, and production-ready.

What you gain

Finance: Credit risk assessment models to reduce default rates

Retail: Product recommendation engines to increase average order value

Healthcare: Diagnosis support systems that assist care staff

What is included

What we put in place, within the scope agreed together during the scoping call.

Supervised learning

Regression and classification for predictive modeling
Unsupervised learning

Clustering, dimensionality reduction, and anomaly detection
Deep learning

Neural networks for complex pattern recognition
Natural language processing

Text analysis, sentiment analysis, and language understanding
Computer vision

Image recognition, object detection, and video analysis
Time series forecasting

Demand prediction and trend analysis
Recommendation systems

Personalized content and product suggestions

How we work

Step 01
Problem definition & data exploration

We clearly define the business problem, success metrics, and explore available data to assess feasibility.
Step 02
Feature engineering & data preparation

We engineer relevant features, clean data, handle missing values, and prepare datasets for modeling.
Step 03
Model development & training

We select appropriate algorithms, train multiple models, and compare performance using rigorous validation techniques.
Step 04
Model evaluation & tuning

We evaluate model performance, optimize hyperparameters, and ensure robustness through cross-validation.
Step 05
Deployment & monitoring

We deploy models to production, establish monitoring systems, and continuously improve performance based on feedback.

The tools we use

We choose each tool for your project, your team and your budget.

Python: Primary language with scikit-learn, TensorFlow, PyTorch, and Keras
TensorFlow: Deep learning framework for neural networks
PyTorch: Flexible ML framework for research and production
Jupyter: Interactive development and documentation environment
MLflow: ML lifecycle management and experiment tracking

Frequently asked questions

The questions we are asked most often about this service.

Data analysis focuses on describing past trends and patterns, while data science uses advanced algorithms and machine learning to predict future outcomes and automate decision-making processes.

Dataset size depends on problem complexity. Simple problems may need hundreds of examples, while deep learning typically requires thousands. We can use transfer learning and data augmentation techniques for smaller datasets.

Accuracy depends on the problem and on the quality of your data. We measure it on your own data before going live, and we explain the model's limits.