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Reproducibility of ML system is increasingly important topic in ML community. Reproducibility ensures conclusiveness of the model performance, provides an understanding how ML system works and reduces unnecessary errors when the system is deployed into production. With increasing AI regulation, it will soon become a requirement for many ML applications. In this talk, we will explore different aspects of reproducibility such as reproducibility of the dataset, data processing, ML model, its randomness and hyperparameters, code and SW environment, as well as concepts and practical tools such as data versioning, feature, metadata and artifact store, model registry and containerization that together ensure reproducibility of our experiments.
Reproducibility of ML system is increasingly important topic in ML community. Reproducibility ensures conclusiveness of the model performance, provides an understanding how ML system works and reduces unnecessary errors when the system is deployed into production. With increasing AI regulation, it will soon become a requirement for many ML applications. In this talk, we will explore different aspects of reproducibility such as reproducibility of the dataset, data processing, ML model, its randomness and hyperparameters, code and SW environment, as well as concepts and practical tools such as data versioning, feature, metadata and artifact store, model registry and containerization that together ensure reproducibility of our experiments.
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