I will introduce the central concepts and ideas behind probabilistic programming systems (PPS), and introduce several Python packages that can be used to write probabilistic programs. PPS seek to create a programming environment that allows non-ML-experts to apply Bayesian statistics to solve complex statistics and machine-learning problems. By removing the need to write custom inference code, PPS systems have the potential to empower experts and non-experts alike to perform machine learning and statistical modeling with vastly simpler code. The current state of the art allows for rapid prototyping of complex models on reasonably sized datasets.