Open source framework for scaling out hyperparameter tuning or distributed deep learning.

Unifying Single-host and Distributed Machine Learning with Maggy

Try out Maggy for hyperparameter optimization or ablation studies now on to access a new way of writing machine learning applications.

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AI & Deep Learning for Fraud & AML

Anomaly detection and Deep learning for identifying money laundering . Less false positives and higher accuracy than traditional rule-based approaches.

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One function is all you need for ML Experiments

Hopsworks supports machine learning experiments to track and distribute ML for free and with a built-in TensorBoard.

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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.