Custom Neural Network Solution Development
Neural networks are the right tool for unstructured data (images, text, audio, video) and for tasks with high nonlinearity where traditional ML methods reach their limits. We design architectures, train, and deploy neural networks for specific business tasks.
Architectural Choices
Architecture is determined by data type and task:
Transformers — text, multimodal data, long sequences. BERT-family for understanding, GPT-family for generation. Vision Transformer (ViT) competes with CNN on ImageNet with sufficient data volume.
Convolutional Networks — images and video. EfficientNet, ConvNeXt, ResNet for classification/detection. YOLO-family for real-time object detection. U-Net for segmentation.
Recurrent / State Space Models — time series with long dependencies. LSTM, GRU — classics. Mamba (SSM) — new generation with linear complexity in sequence length.
Graph Neural Networks — molecules, social networks, recommendation systems with explicit connection structure. GCN, GAT, GraphSAGE.
Diffusion Models — image, audio, and 3D generation. DDPM, DDIM, flow matching.
Training
Transfer Learning — fine-tuning pretrained models outperforms training from scratch in 90% of cases. Data requirements decrease 10–100 times.
Fine-tuning Strategies: Full fine-tuning (>10K examples), LoRA/QLoRA (100–10K examples), Prompt Tuning / Prefix Tuning (<<100 examples), zero-shot with proper prompting.
Regularization: Dropout, Label Smoothing, Mixup, CutMix, stochastic depth — choice depends on data type and model size.
Distributed Training: DDP (DistributedDataParallel) for multi-GPU. DeepSpeed ZeRO for models not fitting in single GPU. FSDP (Fully Sharded Data Parallel) as alternative.
Inference Optimization
Training is half the work. Production requires:
- Quantization: INT8 (post-training or QAT), INT4 (bitsandbytes, GPTQ). 2–4x speedup with minimal quality loss
- Pruning: structured pruning for architectural compactness
- Knowledge Distillation: training small model on knowledge of large model (BERT → TinyBERT: 7.5x faster, 96% quality)
- ONNX + TensorRT: compilation for maximum throughput on NVIDIA GPU
Typical Projects
| Task | Architecture | Training Time (A100) |
|---|---|---|
| Image Classification (1000 classes) | EfficientNet-B4 fine-tune | 2–8 h |
| NER for Specialized Domain | BERT-base + CRF head | 4–12 h |
| Time Series Anomaly Detection | Transformer + reconstruction | 6–24 h |
| Custom Object Detection | YOLOv8 fine-tune | 4–16 h |
| Domain-specific LLM | LLaMA 3 8B LoRA | 10–48 h |
Delivery
Trained model in ONNX/TorchScript + inference endpoint + documentation + training pipeline for retraining on new data. Reproducibility through DVC + MLflow.