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 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.
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.