Python Development

Python Backend Development

From high-performance APIs to AI/ML pipelines, we harness Python's versatility to build intelligent backends that power data-driven products. FastAPI, Django, pandas, and the entire scientific Python ecosystem — all in our toolkit.

35+ Python Projects
AI/ML Native
Expert Data Pipeline
api.dataforge.ai
Models
Pipelines
Datasets
Models Deployed
24
Accuracy
97.3%
Active Models
Sentiment Analyzer v3 Production
Fraud Detection Staging
Recommendation Engine Training
35+
Python Projects Delivered
97%
Model Accuracy Avg
<100ms
API Response Time
50TB+
Data Processed
from fastapi import FastAPI, Depends
from pydantic import BaseModel

app = FastAPI()

class Prediction(BaseModel):
  label: str
  confidence: float

@app.post("/predict")
async def predict(data: InputData):
  result = await model.predict(data)
  return Prediction(**result)
FastAPI

Async APIs at Lightning Speed

FastAPI delivers automatic OpenAPI docs, Pydantic validation, and async/await support out of the box. We build type-safe, high-performance REST APIs that handle thousands of concurrent requests with minimal latency — perfect for ML inference endpoints and real-time data services.

  • Auto-generated OpenAPI / Swagger docs
  • Pydantic models for request/response validation
  • Native async/await with ASGI
  • Dependency injection built-in
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline

df = pd.read_parquet("data/train.parquet")
X, y = df.drop("target", axis=1), df["target"]

pipeline = Pipeline([
  ("scaler", StandardScaler()),
  ("model", RandomForestClassifier(
    n_estimators=200,
    max_depth=10
  ))
])
pipeline.fit(X, y)
Data Science & ML

ML Pipelines, Production-Ready

Python's data ecosystem is unmatched. We build end-to-end ML pipelines with pandas for data wrangling, scikit-learn for classical ML, and PyTorch/TensorFlow for deep learning. Every pipeline is versioned, reproducible, and deployed with monitoring from day one.

  • pandas + NumPy for data transformation
  • scikit-learn pipelines for reproducible ML
  • PyTorch / TensorFlow for deep learning
  • MLflow for experiment tracking & deployment
Use Cases

What We Build with Python

AI/ML APIs
Production ML inference endpoints with FastAPI, model versioning, A/B testing, and real-time prediction serving.
Data Pipelines
ETL/ELT workflows with Apache Airflow, Celery, and pandas — processing terabytes of data on schedule or in real-time.
Web Scrapers
Intelligent web scraping with Scrapy, BeautifulSoup, and Playwright — structured data extraction at scale.
Automation Scripts
Business process automation, report generation, file processing, and scheduled tasks that save hours of manual work.
Scientific Computing
Numerical simulations, statistical analysis, and research computing with NumPy, SciPy, and Jupyter notebooks.
Django Web Apps
Full-featured web applications with Django's batteries-included approach — admin panels, ORM, auth, and REST framework.
Python Ecosystem

Tools & Libraries We Rely On

Python Python
FastAPI FastAPI
Django Django
Celery
pandas pandas
SQLAlchemy SQLAlchemy
pytest pytest
Poetry

Ready to Build with Python?

From AI-powered APIs to robust data pipelines — our Python engineers deliver production-grade backends that turn your data into a competitive advantage.

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