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Building efficient machine learning applications is not a simple task. The typical engineering process is an iteration of data wrangling, feature generation, model selection, hyperparameter tuning and evaluation. The amount of possible variations of input features, algorithms and parameters makes it too complex to perform efficiently even by experts. Automating this process is especially important when building machine learning applications for thousands of customers.nnIn this talk I demonstrate
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