API World

Your REST API is great but if your documentation sucks, your customers won't feel the love.    Documentation is often considered only as an afterthought, something separate from the development process. "Are we ready to release? Is the documentation ready? Did somebody check it? - Blank stares" Sounds familiar?    Documentation writing shouldn't be a manual activity decoupled from development. The code has all the information already. You just need to get it out. You need a good documentation generator.    In this talk, we present MireDot - a REST API documentation generator for Java. MireDot excels at the accuracy of the generated documentation. It takes into account complex Java constructs (generics, inheritance) and existing annotations (Jax-RS, Jackson, Javadoc, …) that other generators don't. It requires almost no effort to set up and uses the information that is already present in your source code. Just plug it into your build system and your    documentation rolls out in seconds.  

Cascading Pattern is an open source project that takes models trained in popular analytics frameworks, such as R, SAS, Microstrategy, etc., and runs them at scale on Apache Hadoop. With Pattern, developers can use a Java API to create complex machine learning applications, such as recommenders or fraud detection. Pattern effectively lowers the barrier of adoption to Apache Hadoop for developers because developers can use existing skillsets to immediately begin building these complex applications.    This machine-learning library works by translating PMML – an established XML standard for predictive model markup – into data workflows based on the Cascading API in Java. PMML models can be run in a pre-defined JAR file with no coding required. PMML can also be combined with other flows based on ANSI SQL (Cascading Lingual), Scala (Scalding), Clojure (Cascalog), etc. Multiple companies have collaborated to implement parallelized algorithms: Random Forest, Logistic Regression, K-Means, Hierarchical Clustering, etc. Benefits include greatly reduced development costs and less licensing issues at scale – while leveraging a combination of Apache Hadoop clusters, existing intellectual property in predictive models, and the core competencies of analytics staff.     In this presentation, Concurrent, Inc.’s Alexis Roos, will provide sample code that will show applications using predictive models built in SAS and R, such as anti-fraud classifiers. Additionally, Alexis will compare variations of models for enterprise-class customer experiments.