White Paper Abstract
Java is increasingly relevant to machine, deep and reinforcement learning. Java developers need no longer be restricted to technical computing and statistical programming languages like Python and R, given the emergence of new Java algorithmic frameworks such as DeepLearning4J.
With Java and JVM technologies mainstream in the enterprise stack – Spark, Hadoop, Kafka, Cassandra, Elasticsearch, SAS, Tomcat, Scala, Clojure and others, in most cases made more performant with Azul Zing – Java can be used from concept to production to exploit data science applications in search, fraud detection, adtech, trading, risk management and others.
Who Should Read It?
This paper is written for Data Scientists, Quants and Developers deploying Data Engineering and Data Science algorithms into Java and JVM-based enterprise applications, and for CTOs, Architects and DevOps managing the workflows.
- Common Applications of Data Science
- Definitions: Machine learning, deep learning, data engineering and data science
- About Java Deep Learning
- Why Java for data science workflows, for both production and research?
Claim your free copy of 'Data Science & Machine Learning' white paper now!