Python Development Services
From production APIs and automation to machine learning at scale
Python is our default choice where data and intelligence are central to the product. We build production APIs, data pipelines and machine-learning services — and just as importantly, we get models out of notebooks and into systems your business can depend on.
- High-performance APIs with FastAPI and Django
- Data pipelines, ETL and workflow orchestration
- Machine-learning model training, packaging and deployment
- Process automation and third-party system integration
- Computer vision, NLP and generative AI services
- One language spanning your application and your data science work
- Access to the strongest AI and data ecosystem available today
- Rapid prototyping that shortens the path from idea to validation
- Production-grade deployment, not proof-of-concept notebooks
- AI and ML services
- Data engineering pipelines
- Backend APIs
- Automation and scraping
Common questions
Why Python for a backend rather than Node?
When data or machine learning is central to the product. Keeping the API and the model work in one language removes an entire category of translation bugs and lets the same engineers move between them. For a pure CRUD API with no data science component, either is fine and team familiarity should decide.
FastAPI or Django?
FastAPI for APIs and machine-learning services: async by default, fast, and the OpenAPI documentation comes free from the type hints. Django when you need the full framework - admin interface, ORM, authentication, migrations - and want it working the first week. Both are solid; the question is how much of the framework you will actually use.
How do you get a model out of a notebook and into production?
By treating the notebook as the experiment and not the artefact. That means the training pipeline in version control and reproducible, the model versioned with the data it was trained on, a service with a real API around it, and monitoring in place. Most stalled machine-learning projects are stuck exactly here - the model works and nothing else does.
How do you manage dependencies and environments?
Pinned, locked and reproducible - Poetry or uv rather than a loose requirements file, and containers for anything deployed. Python's packaging is the least pleasant part of the ecosystem and unpinned dependencies are how a build that worked in June fails in September with no code change.