Sitemap - 2022 - Deliberate Machine Learning

TDD or not TDD: How to write tests in Data Science

Why anti-OOP content is wrong?

[Interesting content] Adam Tornhill - A Crystal Ball to Prioritize Technical Debt

MLOps Live with - Writing clean, production-level ML code

Refactoring the Titanic

What's still wrong with MLOps?

Data Science code quality hierarchy of needs

How python programmers save the environment (by making python run faster)?

Can you version control Jupyter notebooks?

How did I change my mind about dataclasses in ML projects?

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