AI & Machine Learning
Deep Learning

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.

Scope

What this service covers

01Classification and regression (tabular data, scikit-learn + XGBoost)
02Computer vision (CNN, VGG, ResNet, YOLO — detection, segmentation, OCR)
03NLP and Large Language Models (transformers, fine-tuning, RAG)
04Time series forecasting (LSTM, Prophet, statsmodels)
05Deep learning on GPU (PyTorch, Keras, TensorFlow)
06Production deployment (ONNX, TorchServe, TF Serving, drift monitoring)
How we work

Cooperation stages

01

Problem and data definition

What we predict, which metrics, what data we have / lack, business baseline

02

Prototype in Jupyter

Data exploration, baseline, architecture choice, hyperparameter experiments

03

Model in production

Packaging (ONNX/TorchScript), API endpoint, A/B test, metrics monitoring

04

Maintenance and retraining

Retraining pipeline, drift alerts, model documentation

FAQ

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.

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Let's start

AI & Machine Learning — let's talk

Free project quote. Audit of the current state and a concrete next-step proposal.