Sparkling Water integrates H2O’s fast scalable machine learning engine with Spark. Sparkling Water excels in leveraging existing Spark based workflows that need to call advanced machine learning algorithms (Malohlava, Tellez, & Lanford, 2015). A typical example involves data “munging” with the help of the Spark API, in which a prepared table is passed to the H2O Deep Learning algorithm (Malohlava, Tellez, & Lanford, 2015). The constructed Deep Learning model estimates different metrics based on the testing data which can be used in the rest of the Spark workflow (Malohlava, Tellez, Lanford, 2015). More details can be found from the website https://h2o-release.s3.amazonaws.com/h2o/rel-slater/9/docs-website/h2o-docs/booklets/SparklingWaterVignette.pdf.
Published in Chapter:
The Image as Big Data Toolkit: An Application Case Study in Image Analysis, Feature Recognition, and Data Visualization
Kerry E. Koitzsch (Kildane Software Technologies Inc., USA)
Copyright: © 2018
|Pages: 52
DOI: 10.4018/978-1-5225-3142-5.ch018
Abstract
This chapter is a brief introduction to the Image As Big Data Toolkit (IABDT), a Java-based open source framework for performing a variety of distributed image processing and analysis tasks. IABDT has been developed over the last two years in response to the rapid evolution of Big Data architectures and technologies, distributed and image processing systems. This chapter presents an architecture for image analytics that uses Big Data storage and compression methods. A sample implementation of our image analytic architecture called the Image as Big Data Toolkit (IABDT) addresses some of the most frequent challenges experienced by the image analytics developer. Baseline applications developed with IABDT, status of the toolkit and directions for future extension with emphasis on image display, presentation, and reporting case studies are discussed to motivate our design and technology stack choices. Sample applications built using IABDT, as well as future development plans for IABDT are discussed.