
AI & Machine Learning
Machine learning models, neural networks, computer vision and NLP. From prototype to production.
Service philosophy
AI is not just ChatGPT — it's specific models solving specific business problems. Image classification, demand forecasting, document analysis, anomaly detection. We work with PyTorch, Keras and TensorFlow depending on the project. From model calibration on client metrics, through deployment (ONNX, TorchServe, TF Serving), to drift monitoring in production.
What this service covers
Cooperation stages
Problem and data definition
What we predict, which metrics, what data we have / lack, business baseline
Prototype in Jupyter
Data exploration, baseline, architecture choice, hyperparameter experiments
Model in production
Packaging (ONNX/TorchScript), API endpoint, A/B test, metrics monitoring
Maintenance and retraining
Retraining pipeline, drift alerts, model documentation
Most frequently asked questions
01Do we need our own training data?
Often yes — especially for industry-specific classification, OCR of custom forms, demand prediction. For some tasks transfer learning on a pre-trained model (VGG/ResNet/BERT) is enough — 100-1000 examples may suffice.
02PyTorch or TensorFlow?
Depends on the project. PyTorch — research, NLP, transformers, flexibility. TensorFlow/Keras — production deployment, mobile (TF Lite), edge devices, Google ecosystem. Often a mix.
03How long does model deployment take?
MVP classification model: 4-8 weeks. Computer vision with custom data: 8-16 weeks. LLM fine-tuning + RAG: 6-12 weeks. Times depend mainly on data quality — clean data = faster.
AI & Machine Learning — let's talk
Free project quote. Audit of the current state and a concrete next-step proposal.
