Description
Temporal data is ubiquitous in data science and plays a vital role in machine learning pipelines and business decisions. Preprocessing temporal data using generic data tools can be tedious, lead to inefficient computation, and be prone to errors. Temporian is an open-source library for safe, simple, and efficient preprocessing and feature engineering of temporal data. It supports common temporal data types, including non-uniform sampled, multi-variate, multi-index, and multi-source data. Temporian favors interactive development in notebooks and integration with other machine learning tools, and can run at scale using distributed computing. This talk, aimed at data scientists and machine learning practitioners, will showcase Temporian’s key features along with its powerful API, and demonstrate its advantages over generic data preprocessing libraries for handling temporal data.