The modern Python ecosystem shortens the distance between idea and implementation. This talk presents a focused workflow to move from a business question to a working prototype, fast. We’ll explore reproducible environments (uv, Docker), quick data iteration with polars and duckdb, clean project scaffolding (pyproject.toml), and lightweight service layers with FastAPI and pydantic. Along the way, we’ll integrate tests (pytest), static checks (mypy), and fast linting (ruff). You’ll leave with a reusable structure, toolchain recommendations, and a mental model for optimizing feedback loops and development in modern Python projects.
This talk outlines a practical, opinionated workflow for building real things quickly using modern Python without relying on heavy frameworks or over-engineering.
Core idea:
- The shortest path from notebook to usable component is a repeatable, well-lit toolchain with the right structure.
- 🪜 Scaffold a clean project using
pyproject.toml, deterministic environments (uv), and lightweight automation (e.g. Makefile or CLI scripts). - 🔍 Explore data rapidly with polars and duckdb, capturing the business logic in small, testable functions.
- 🎀 Wrap the logic in a minimal FastAPI app with pydantic validation, creating clean contracts and boundaries.
- ✚ Add fast feedback mechanisms: tests with pytest, type safety via mypy, and low-friction code hygiene using ruff and pre-commit.
- 📦 Package a handoff-friendly interface (command-line entrypoints, minimal docs) for teammates or deployment pipelines.
This talk isn’t a showcase of cutting-edge libraries. It’s a field guide on how to leverage modern Python tools and fostering repeatable software engineering habits to maximize value delivery.
You’ll leave with:
- 🗺️ A blueprint for rapid iteration.
- 🔄 Reusable patterns for API-bound prototyping.
- 🧠 A mindset that treats reproducibility as a first-class concern.
- Basic Python (functions, environments), familiarity with DataFrame operations, and HTTP/JSON fundamentals.
Tools and Frameworks:
We will introduce you to certain modern frameworks in the workshop but the emphasis be on first principles and using vanilla Python and LLM calls to build AI-powered systems.
César Soto Valero
César is currently a Data Scientist at SEB Group, where he develops AI models to enhance the security of financial transactions on a global scale. He completed an M.Sc. in Machine Learning and moved to Sweden in 2018 to pursue a Ph.D. in Computer Science at KTH Royal Institute of Technology. During his five years at KTH, he pioneered open-source tools and techniques to mitigate software bloat, contributing to the efficiency and security of modern software systems. César is deeply passionate about AI, science, and technology, with a strong focus on bridging cutting-edge research with real-world applications. He is dedicated to advancing AI’s role in building smarter, more resilient systems that drive innovation.
Outline
Citation
@online{bochman2025,
author = {Bochman, Oren},
title = {From {Ideas} to {APIs:} {Delivering} {Fast} with {Modern}
{Python}},
date = {2025-12-10},
url = {https://orenbochman.github.io/posts/2025/2025-12-10-pydata-ideas-to-apis/},
langid = {en}
}