Start a projectStart a projectDjango, FastAPI, data pipelines, and ML infrastructure — Python's ecosystem is unmatched for turning raw data into business intelligence and production APIs.
Web APIs, data engineering, and machine learning — we cover Python's full range.
Async Python APIs with automatic OpenAPI docs, dependency injection, and Pydantic validation — the fastest way to build production-grade REST endpoints.
The batteries-included framework for content-heavy platforms — admin panel, ORM, auth, and middleware that gets you to market fast.
Extract, transform, load — we build data pipelines that turn raw sources into clean, queryable datasets your analytics team can trust.
Model training, experiment tracking, and serving infrastructure — so your data science team can focus on models, not DevOps.
The frameworks, libraries, and tools that make Python unbeatable for data-driven backends.
Real-time transaction scoring service using scikit-learn models served via FastAPI — processing 10k transactions per second with sub-50ms latency.
HIPAA-compliant ETL pipeline ingesting HL7/FHIR records, normalizing clinical data, and loading into a Snowflake warehouse for population health analytics.
Collaborative filtering + content-based hybrid recommendation system serving personalized product suggestions to 2M daily active users.
PostGIS-backed API for fleet management with real-time vehicle tracking, route optimization, and geofence alerting across 5k vehicles.
OCR and NLP pipeline extracting structured data from invoices, contracts, and forms — with human review queues for low-confidence extractions.
Airflow-orchestrated pipeline ingesting satellite imagery and weather station data, computing carbon footprint metrics for ESG reporting.
Type-safe endpoints, auto-generated docs, and dependency-injected model serving.
# FastAPI with Pydantic v2 + dependency injection from fastapi import FastAPI, Depends from pydantic import BaseModel class PredictionRequest(BaseModel): features: list[float] model_version: str = "v2.1" app = FastAPI() @app.post("/predict") async def predict( req: PredictionRequest, model = Depends(get_ml_model), ): score = model.predict([req.features]) return {"score": score[0], "version": req.model_version}
FastAPI for APIs, microservices, and ML serving — it's async-first, faster, and generates OpenAPI docs automatically. Django for content-heavy platforms where you need the admin panel, ORM, and auth out of the box. We often use both in the same organization.
With async FastAPI and uvicorn, Python handles thousands of concurrent requests efficiently. For CPU-bound work, we use multiprocessing, Celery workers, or Rust/Go sidecars. The bottleneck is almost always the database, not the runtime.
uv for fast dependency resolution and virtual environments. pyproject.toml as the single config file. Docker containers for production with multi-stage builds. We pin exact versions and use Dependabot for security updates.
Absolutely. We refactor notebook logic into tested Python modules, build FastAPI endpoints or Airflow DAGs around them, add logging and monitoring, and set up CI/CD. The data scientists keep iterating in notebooks; we keep the production pipeline stable.
Let's build Python systems that turn your data into a competitive advantage.
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