PFA encapsulates a unit of data processing called an inference engine.
It provides a common interface to safely deploy analytic workflows across environments, from embedded systems to distributed data centers.
PFA is an open standard for statistical models, machine learning models and data transformation engines. PFA combines the ease of portability across systems with algorithmic flexibility: models, pre-processing, and post-processing are all functions that can be arbitrarily composed, chained, or built into complex workflows. PFA may be as simple as a raw data transformation or as sophisticated as advanced ML/AI algorithms, all described as a JSON or YAML configuration file.
PFA Open Inference Engine(POIE) is reference implementations of the PFA specification. The Portable Format for Analytics (PFA) is a specification for scoring engines: event-based processors that perform predictive or analytic calculations. It is a common language to help smooth the transition from statistical model development to large-scale and/or online production. For a model expressed as PFA to be run against data, an application is required.
Learn more about PFA by following the interactive tutorials or browsing the reference material in the navigation bar.