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.
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)
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)
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
What We Build with Python
Tools & Libraries We Rely On
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.