AI & Generative AI

Machine learning engineering

Forecasting, classification, recommendation and optimization models built for production, not just notebooks.

Überblick

Classical machine learning still drives a large share of enterprise AI value: demand forecasts, churn and risk scores, recommendations, anomaly detection and pricing or scheduling optimization. The hard part is rarely training a model. It is making the model reliable, explainable and embedded in a real decision.

Our ML engineers work backward from the decision. We agree what the model must predict, how its output will be used and what good performance looks like, then build features, models and evaluation against that target. We write production code from the start, with versioned data, reproducible training, automated tests and clear documentation of assumptions and limitations.

Models are deployed as batch jobs, APIs or embedded scoring inside the systems your teams already use, with monitoring for accuracy and drift. Where decisions affect customers or are subject to regulation, we build in explainability and the evidence your model risk or compliance teams will ask for.

Was Sie erhalten

01Problem framing with agreed metrics and decision integration
02Feature pipelines and trained, validated models
03Production deployment as batch, API or embedded scoring
04Model documentation covering data, assumptions, limitations and explainability
05Monitoring for performance and drift, with retraining triggers

Technologien, mit denen wir arbeiten

Python
scikit-learn
XGBoost and LightGBM
PyTorch
Databricks
Amazon SageMaker
Azure Machine Learning
MLflow

Wir sind technologieunabhängig: Werkzeuge wählen wir passend zu Ihren Anforderungen und bestehenden Investitionen aus.

Fragen, die wir häufig hören

Should we use machine learning or generative AI for this problem?

It depends on the task. Structured prediction problems such as forecasting, scoring and optimization are usually better served by classical ML, which is cheaper, faster and easier to validate. We often combine the two, for example using a language model to extract features from documents that feed a traditional model.

How do you make models explainable?

We choose model types that suit the level of explainability required and use techniques such as feature importance and SHAP values to show what drives predictions. We also document limitations and intended use so reviewers and business users know when to rely on the output.