Try out Maggy for hyperparameter optimization or ablation studies now on Hopsworks.ai to access a new way of writing machine learning applications.
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Maggy is a framework for distribution transparent machine learning experiments on Apache Spark.
Maggy lets you reuse training codes whether you are training small models on a laptop or scaling out hyperparameter tuning or distributed deep learning on a cluster.
Maggy can replace the current waterfall development process for distributed ML applications, where code is rewritten at every stage in order to account for the different distribution context.
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