Building NLP Systems: Pipelines, Models, and Deployment

NLP projects often start with a misconception: "We'll take BERT and it will just work." A month later, latency is too high for production, the model weighs 1.5 GB, and F1 on Russian text is 0.6. We've seen dozens of such projects. The problem isn't the model—it's the lack of a systematic approach to the pipeline. We build production-ready NLP systems for Russian language that actually work in production: with data drift control, metric monitoring, and architecture selection driven by the task, not by the trend. In NLP system development, we combine rigorous Russian morphology analysis with efficient model selection to build cost-effective solutions. Processing natural language in a Russian context requires accounting for morphology, choosing the right pipeline, and applying MLOps practices.

Problems We Solve

• Russian morphology. The word "разработка" has 12 forms. Without lemmatization, TF-IDF loses 40% of meaning. We use pymorphy3 or natasha—they provide lemmas with >95% accuracy for technical texts. pymorphy3 documentation confirms 97% accuracy for literary text.
• Data drift. A month after deployment, the token distribution shifts. We automatically detect drift and trigger a retraining cycle. Without this, F1 drops 10–15% per quarter.
• Architecture choice. 80% of classification tasks are solved by Logistic Regression + TF-IDF with F1 0.92–0.95. Fine-tuning BERT is needed only when data is scarce (<5k examples) or semantic complexity is high (sarcasm, context dependency).

How We Do It: A Case from Our Practice

A fintech startup client needed to classify customer inquiries into 12 categories (complaint, return, consultation). Data: 50k labeled messages. Our approach:

• Analysis: class imbalance (3 classes accounted for 70% of samples).
• Prototype: FastText + TF-IDF. F1 = 0.91. Inference time 2 ms on CPU.
• Comparison: fine-tune BERT-base: F1 = 0.93, but latency 150 ms on GPU and 20× higher inference cost. FastText outperformed BERT in speed by 75× with comparable quality. Moreover, Logistic Regression + TF-IDF is 10× cheaper than BERT with similar accuracy.
• Result: we used FastText, added rule-based correction for rare classes. F1 = 0.93, deployed on 2 CPUs, reducing monthly infrastructure costs from $3000 to $300 for 1M predictions.

Lesson: lightweight solution + smart rules often beat a heavy transformer.

How to Choose NLP Model for Your Task

Why Morphology Is the Main Pain of Russian NLP

In English, tokenization is trivial: split by spaces. In Russian, "разработанный" and "разработана" are distinct tokens that don't look alike. Without lemmatization, the model cannot generalize. Transformers like BERT require careful tokenization; using a SentencePiece tokenizer helps but morphological analysis is still beneficial. We use pymorphy3, which gives lemmas with 97% accuracy for literary text and 93% for technical text. For NER, we use natasha, which considers context and outputs BIO-format tags. Russian morphology analysis is a mandatory step in any NLP pipeline. We also consider precision-recall trade-off and AUC-ROC when evaluating models.

Framework Comparison for Russian

For production, spaCy is usually sufficient. DeBERTa is only needed when maximum quality is critical. We often use model distillation to reduce BERT size by 40% with minimal accuracy loss, and ONNX runtime for efficient CPU inference.

Our Process

1. Analytics — gather requirements, audit data, select metrics (F1, latency, cost).
2. Prototype — MVP in 1–2 weeks: pipeline with lightweight models, establish baseline.
3. Training — if needed: fine-tune transformers, augment data, distill models.
4. Deployment — Docker, FastAPI, Triton inference server (for GPU). CI/CD with data drift tests.
5. Monitoring — log metrics, set alerts when F1 drops >5%.

What's Included

• Pipeline code repository (Python, PyTorch/TensorFlow)
• Architecture and API documentation (OpenAPI)
• Configured CI/CD (GitHub Actions / GitLab CI)
• Monitoring stack (Prometheus + Grafana dashboard)
• Client team training (2–3 workshops)
• 3 months of post-deployment support

Estimated Timelines

• Prototype (basic pipeline): 1–2 weeks
• Production solution (single task): 3–5 weeks
• Comprehensive NLP platform (multiple tasks): 2–4 months

Pricing is determined after analysis—contact us to discuss your project.

Why Choose Us

• Proven track record in AI solutions, with 5+ years of NLP experience, 30+ NLP projects delivered (fintech, e-commerce, healthcare), and 10+ active clients
• Experience with OpenAI, Yandex GPT, Hugging Face
• Certified MLOps specialists (Kubeflow, MLflow) with 5+ years in the field

Get in touch for a free consultation on your project.

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from pymorphy3 import MorphAnalyzer
import re

morph = MorphAnalyzer()

def preprocess(text):
    tokens = re.findall(r'[а-яё]+', text.lower())
    lemmas = [morph.parse(tok)[0].normal_form for tok in tokens]
    return ' '.join(lemmas)

# Example usage
vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform([preprocess(t) for t in train_texts])
model = LogisticRegression().fit(X_train, train_labels)