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Clean Machine Learning Code: Practical Software Engineering Principles for ML Craftsmanship


Machine learning pipelines are software pipelines after all. Their complexity and design viscosity lead to spectacular, costly and even deadly ML failures. This talk describes the most important Clean Code and Clean Architecture design principles, applied to machine learning applications. It aims to help the audience reduce machine learning technical debt, and to design robust ML architectures.


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