Inference QL: Al for data engineers in Clojure
Ulrich Schaechtle at Clojure/conj 2019
InferenceQL, MIT’s new open-source, Clojure-based AI platform for sparse and semi-structured data, empowers data engineers to use AI to explore, monitor, clean, and predict data streams without having to learn probability theory and computational statistics.
Users can build models using automatic model discovery, then query these models using a simple, SQL-like language and a Clojure API; the platform also provides a spreadsheet interface with built-in data visualization.
This talk will show how Clojure and ClojureScript’s distinctive features enabled us to implement and scale up InferenceQL. It will also show how Clojure programmers can use InferenceQL to quickly get started with probabilistic programming, and how InferenceQL complements the growing Clojure ecosystem for data engineering and data science.
Because InferenceQL is implemented in Clojure / ClojureScript, compiling to both the JVM and JavaScript, it can drive interactive data experiences in web pages and be part of enterprise data pipelines for uses including data engineering, analytics consulting, data journalism, as well as scientific data analysis and research in probabilistic programming, causal modeling, and probabilistic expert systems.
Users can build models using automatic model discovery, then query these models using a simple, SQL-like language and a Clojure API; the platform also provides a spreadsheet interface with built-in data visualization.
This talk will show how Clojure and ClojureScript’s distinctive features enabled us to implement and scale up InferenceQL. It will also show how Clojure programmers can use InferenceQL to quickly get started with probabilistic programming, and how InferenceQL complements the growing Clojure ecosystem for data engineering and data science.
Because InferenceQL is implemented in Clojure / ClojureScript, compiling to both the JVM and JavaScript, it can drive interactive data experiences in web pages and be part of enterprise data pipelines for uses including data engineering, analytics consulting, data journalism, as well as scientific data analysis and research in probabilistic programming, causal modeling, and probabilistic expert systems.