Sitemap - 2022 - Laszlo’s Newsletter

Slides for my talk at PyData London 2022

User Comments on: "How can a Data Scientist refactor Jupyter notebooks towards production-quality code?"

Industrial Scale Text Classification

Clean Architecture in Data Science (Part 1)

Getting Data Scientists to Write Better Code 🔥 with Laszlo Sragner

Documentation vs Documentation in Data Science

AI and COVID: A lesson of ML product design

Article Review: Rendezvous Architecture for Data Science in Production by Jan Teichmann

Article Review: Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology by Mercedes-Benz AG and TU Berlin

Article Review: Machine Learning operations maturity model by Microsoft

Article Review: MLOps: Continuous delivery and automation pipelines in machine learning by Google

Article Review: Rules of Machine Learning: Best Practices for ML Engineering by Google

Article Review: The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction by Google

Article Review: Hidden Technical Debt in Machine Learning Systems by Google

Article Review: Machine Learning: The High-Interest Credit Card of Technical Debt by Google

3 Ways Domain Data Models help Data Science Projects

Unpopular Opinion: “Data-centric AI” is a straw man argument

How Data Scientists cheat in Wordle?

5 Minimalist Tips for Data Scientists to reduce frustration while working with Pandas

Causes of Machine Learning’s productivity problem

3 paradoxes of rule-based Machine Learning systems

Unpopular Opinion: Agile is not only suitable for Data Science projects, but it is the only way to run one

Simple trick to optimise code and maintain readability in a compute heavy application

Causes of Machine Learning’s productivity problem: Process

Causes of Machine Learning’s productivity problem: Metrics

Causes of Machine Learning's productivity problem: Technology

Causes of Machine Learning's productivity problem: Teams

Causes of Machine Learning’s productivity problem: Strategy