# Deployable probabilistic programming

## TL;DR

This paper introduces Infergo, a probabilistic programming framework for Go, providing design guidelines for deployment in production systems and demonstrating its applicability and performance through case studies and benchmarks.

## Contribution

It presents a set of design guidelines for deploying probabilistic programming in production and introduces Infergo as a reference implementation for Go.

## Key findings

- Infergo is effective for various use cases.
- Infergo performs competitively on benchmarks.
- Design guidelines facilitate integration into production systems.

## Abstract

We propose design guidelines for a probabilistic programming facility suitable for deployment as a part of a production software system. As a reference implementation, we introduce Infergo, a probabilistic programming facility for Go, a modern programming language of choice for server-side software development. We argue that a similar probabilistic programming facility can be added to most modern general-purpose programming languages. Probabilistic programming enables automatic tuning of program parameters and algorithmic decision making through probabilistic inference based on the data. To facilitate addition of probabilistic programming capabilities to other programming languages, we share implementation choices and techniques employed in development of Infergo. We illustrate applicability of Infergo to various use cases on case studies, and evaluate Infergo's performance on several benchmarks, comparing Infergo to dedicated inference-centric probabilistic programming frameworks.