AI News, End-to-End Machine Learning with GOAI

End-to-End Machine Learning with GOAI

In May 2017, MapD along with H2O.ai and Continuum Analytics announced theGPU Open Analytics Initiative (GOAI), with the goal of accelerating end-to-end analytics and machine learning on GPUs.

By adopting an existing standard, it is easier to tie in the GDF with the ecosystem and infrastructure already built around Arrow, allowing MapD and others in GOAI to leverage existing features like Parquet-to-Arrow conversion.

While GPUs can provide 100x more processing cores and 20x greater memory bandwidth than CPUs, systems and platforms are unable to harness these disruptive performance gains because they remain isolated from each other.

The initial GOAI prototype integrated the GDF to allow seamless passing of data among processes running on the same GPUs and was shown to provide significant speedups (i.e.

The net result enables lightning-fast interactive data exploration and analysis, feature selection, model training, and model validation by virtue of avoiding any serialization overhead when moving data between processes.

We were able totransformthe process of machine learning into an interactive experience by outputting MapD query results into aGPU Data Frame (GDF)and piping them directly to Anaconda and H2O.ai for further processing.

While the machine learning algorithms themselves typically get most of the attention, the data science workflow around exploring data, feature engineering, and iterative model training usually takes most of a data scientist’s time.

As you can see, building an accurate predictive model is a highly iterative process that benefits from being able to visually explore the data at interactive speeds.The end-to-end machine learning powered by GOAI helps to: A

GDFs break down the silos between systems and software to enable interactive data exploration, feature engineering, model training and model scoring.

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