Data collection for a task is expensive, training from scratch takes weeks. Our custom neural network solutions solve such problems every day: we select architectures, optimize training, deploy solutions that work in production. Over the past 10 years, we have completed more than 50 projects in Computer Vision and NLP: from defect detection on a conveyor line to generating technical documentation. A typical task is classification of 1500 classes on 200K images with 99.2% accuracy and 15 ms latency on CPU. For medical images, we achieved sensitivity improvement from 85% to 97% after fine-tuning on 500 labeled images. Project durations typically range from 2 weeks to 3 months. Typical project costs range from $5,000 to $50,000. By using transfer learning, we saved a client $40,000 compared to training from scratch. Contact us for a consultation and timeline estimate.
Our neural network development services cover the entire lifecycle, from prototyping to production. For neural network training, we recommend starting with pre-trained models to save time and data.
What tasks can neural network solutions solve?
AI models cover a wide range of business tasks: object classification and detection, image segmentation, natural language processing (NLP), content generation, time series forecasting. Each task requires a specific architecture and training strategy. We use PyTorch and Hugging Face Transformers for rapid prototyping, and for production we apply ONNX and TensorRT for maximum performance.
Choosing an Architecture for Neural Network Solutions
The architecture is determined by the data type and task. For text — transformers: BERT-family for understanding, GPT-family for generation. For images and video — ConvNeXt, EfficientNet, YOLO-family for real-time. Time series — LSTM, GRU, or Mamba (SSM) with linear complexity. Graph data — GCN, GAT. For generation — diffusion models (generative models) — DDPM, flow matching. Vision Transformer competes with CNN on ImageNet given sufficient data.
| Architecture | Data | Examples |
|---|---|---|
| CNN | Images, video | EfficientNet, YOLO |
| Transformer | Text, multimodal | BERT, GPT |
| GNN | Graphs | GCN, GAT |
Why Transfer Learning is More Efficient Than Training from Scratch
Transfer Learning — fine-tuning pre-trained models. Data requirements are reduced by 10–100 times compared to training from scratch. Full fine-tuning (>10K examples), LoRA/QLoRA (100–10K), Prompt Tuning (<<100). Regularization: Dropout, Label Smoothing, Mixup. For distributed training we use DDP, DeepSpeed ZeRO, FSDP. In one project with 500 images and LoRA, we achieved 94% accuracy in 4 hours on a single A100. LoRA fine-tuning is 2–3 times faster than full fine-tuning.
Timeline and cost details
Project timelines range from 2 weeks (adapting a pre-trained model) to 3 months (training a large model from scratch). Costs are calculated individually after analyzing the task, typically $5,000–$50,000.How do we optimize inference for production?
Training is only half the battle. Production requires:
- Quantization: INT8 (post-training or QAT) provides 2–4x speedup with <0.5% accuracy loss. For LLMs — INT4 (bitsandbytes, GPTQ).
- Pruning: structured pruning for architectural compactness.
- Knowledge Distillation: BERT → TinyBERT is 7.5x faster with 96% quality.
- ONNX + TensorRT: compilation for maximum throughput on NVIDIA GPUs.
Our inference optimization methods include quantization, pruning, and distillation.
Comparison of optimization methods:
| Method | Speedup | Accuracy Loss | Application |
|---|---|---|---|
| INT8 PTQ | 2–3x | <0.5% | All models |
| INT4 GPTQ | 3–4x | <1% | LLMs >7B |
| Pruning (50%) | 1.5x | 1–2% | CNNs |
| Distillation | 2–7x | 2–5% | BERT, T5 |
Process
- Analysis of task and data — metrics, latency/throughput requirements.
- Architecture design — selection of base model and training strategy.
- Data preparation — augmentation, balancing, labeling.
- Training and validation — tracking via MLflow, iterations.
- Optimization — quantization, pruning, compilation.
- Deployment and monitoring — MLOps: CI/CD for models, automatic retraining on drift.
Deliverables
| Component | Description |
|---|---|
| Model card | Documentation: architecture, metrics, limitations |
| Model weights | ONNX/TorchScript, quantized versions |
| Inference endpoint | Docker container with REST API |
| Training pipeline | Code for retraining on new data (DVC + MLflow) |
| Monitoring | Quality metrics, logs, dashboard |
| Warranty | 3 months of support and bug fixes |
Timeline Estimates
From 2 weeks (adapting a pre-trained model) to 3 months (training a large model from scratch). The cost is calculated individually after analyzing the task.
Typical Mistakes in Neural Network Development
- Ignoring data drift — model degrades within weeks.
- Training on the wrong metric — business metric does not correlate with loss.
- Lack of reproducibility — seed, library versions, environment.
Our team's experience — over 10 years in AI/ML, dozens of successful projects. We guarantee transparency and reproducibility. Order the development of a neural network solution, and we will propose the optimal architecture for your task. Contact us for an engineer consultation — get architecture recommendations and a timeline estimate.