As companies strive to implement modern solutions based on deep learning frameworks, there is a need to deploy it on existing hardware infrastructure in a scalable and distributed manner comes to the fore. Recognizing this need, Cloudera’s and Intel’s Big Data Technologies engineering teams jointly detail Intel’s BigDL Apache Spark deep learning library on the latest release of Cloudera’s Data Science Workbench. This collaborative effort allows customers to build new deep learning applications with BigDL Spark Library by leveraging their existing homogeneous compute capacity of Xeon servers running Cloudera’s Enterprise without having to invest in expensive GPU farms and bringing up parallel frameworks such as TensorFlow or Caffe.
The emergence of “Big Data” has made machine learning much easier because the key burden of statistical estimation—generalizing well to new data after observing only a small amount of data—has been considerably lightened. In a typical machine learning task, the goal is to design the features to separate the factors of variation that explain the observed data. However, a major source of difficulty in many real-world artificial intelligence applications is that many of the factors of variation influence every single piece of data we can observe.
Learn how to use OCR tools, Apache Spark, and other Apache Hadoop components to process PDF images at scale.
Optical character recognition (OCR) technologies have advanced significantly over the last 20 years. However, during that time, there has been little or no effort to marry OCR with distributed architectures such as Apache Hadoop to process large numbers of images in near-real time.
In this post, you will learn how to use standard open source tools along with Hadoop components such as Apache Spark,