Service

Python Development

Python services for AI pipelines, data processing, and APIs where the ecosystem fits—FastAPI, workers, and notebooks that graduate into production code.

Problems this work usually addresses

  • Notebooks that became production without tests
  • Batch jobs without monitoring
  • AI experiments without packaging

Who benefits most

Teams with data/AI workloads that need production packaging without losing iteration speed.

Typical outcomes

  • AI and data work close to the product
  • Reusable services and scripts
  • Clean handoff to ops teams

Technical approach

01

Discovery

Separate experimentation from serving. Define inputs, outputs, and SLAs.

02

Design & architecture

Package services with typed models, config, and dependency pins.

03

Build, test, and handoff

Add tests around parsing and critical transforms before widening traffic.

Architecture considerations

API layer for sync work; workers for heavy jobs; shared libraries for schemas.

Security

Dependency scanning, secret management, and least-privilege cloud roles.

Scaling

Batch sizing and queue depth tuned to data volume. GPU choices only when measured need exists.

InterfacesWeb apps, portals, admin consolesApplication servicesAuth, domain rules, validation, jobsData & eventsPostgres, object storage, audit logsIntegrationsCRM, email, payments, model APIs

Technology stack

PythonFastAPIPydanticWorkersPostgres or object storage

Common challenges

  • Environment drift
  • Heavy dependencies
  • Silent data quality issues

Frequently asked questions

Can Python services sit beside a Next.js app?

Yes. We often keep product UI in Next.js and specialized pipelines in Python behind clear HTTP or queue contracts.

Related services

Relevant projects

Python workloads ready for production packaging?

Share the notebook or script and the reliability bar you need.

Productionize Python services