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

End-to-End Machine Learning with GOAI

In May 2017, MapD along with 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 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.

MapD & GPU-powered Visualization, Data Analysis and Machine Learning

This presentation was recorded at #H2OWorld 2017 in Mountain View, CA. Learn more about Follow @h2oai: ..

O'Reilly AI NYC 2017 : Learn how a GPU database helps you deploy an easy-to-use scalable AI solution

Artificial intelligence's promise is to change how we work and live. With cognitive applications in healthcare, retail, financial services, manufacturing, and ...

MapD & GPU-powered Visualization and Machine Learning

A revolution is taking place in the GPU software stack in the fields of analytics, machine learning and deep learning, driven by NVIDIA's hardware innovation, ...

GPU-powered Interactive and Machine Learning

This was a recording of meetup held in San Francisco on 10/5/2017. A revolution ..

Volkswagen Uses MapD to Visualize and Interrogate Black Box AI Models

Zach Izham (VOLKSWAGEN) and Asghar GHORBANI (VOLKSWAGEN) In the world of analytics and AI for many, GPU-accelerated analytics is equivalent to ...

500M+ rows of retail data in MapD

Watch Todd Mostak identify potential alpha nuggets in this large alternative dataset featuring e-commerce data.

Very Large Data Files, Object Stores, and Deep Learning—Lessons Learned

"In this session, IBM will present details on advanced Apache Spark analytics currently being performed through a collaborative project with the SETI Institute, ...

Predicting the Winning Team with Machine Learning

Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn ...

Augmenting Solr’s NLP Capabilities with Deep-Learning Features to Match Images: Kumar Shubham

Matching images with human-like accuracy is typically extremely expensive. A lot of GPU resources and training data are required for the deep-learning model ...

terrace1 multi-person tracking results, color features vs deep features

terrace1: 4 camera multi-person tracking seqeunce, 9 individuals, sequence from EPFL tracking group ( Top row: tracking with color ..