Meetup #105

Code Quality in Data Science

You hear a lot of advice on applying code craft paradigms to Machine Learning, but what exactly do you need to do? In this presentation, Laszlo introduces a minimal set of techniques that will help remove technical debt and make it more productive. Laszlo talks about: Clean Architecture, Design Patterns, Code Smells, Refactoring, Code Readability, and how these come together in a Data Science project.

In this episode

Laszlo Sragner

Laszlo Sragner

Founder, Hypergolic

Laszlo worked as a quant researcher at multiple investment managers and as a DS at the world's largest mobile gaming company. As Head of Data Science at Arkera, he drove the company's data strategy delivering solutions to Tier 1 investment banks and hedge funds. Laszlo currently runs Hypergolic (hypergolic.co.uk) an ML Consulting company helping startups and enterprises bring the maximum out of their data and ML operations.

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Jose Navarro

Jose Navarro

Host

Jose Navarro is a Machine Learning Infrastructure Engineer making everyday cooking fun at Cookpad, where its recipe platform has more than 40 million monthly users. He holds an MSc in Machine Learning and High-Performance Computing from the University of Bristol. He is interested in Cloud Native technologies, serverless, and event-driven architecture.