Jules Damji

Existing production machine learning systems often suffer from various problems that make them hard to use. For example, data scientists and ML practitioners often spend most of their time stitching and managing bespoke distributed systems to build end-to-end ML applications and push models to production.

This is an introductory and hands-on guided tutorial of Ray2.0 that covers an introductory, hands-on coding tour through the core features of Ray, which provides powerful yet easy-to-use design patterns for implementing distributed systems in Python.

Data Science and ML development bring many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work.

We are amidst the Big Data Zeitgeist era in which data comes at us fast, in myriad forms and formats at intermittent intervals or in a continuous stream, and we need to respond to streaming data immediately.