Task Automation with Zapier, Python, and OpenAI is a 5-day course that teaches developers to automate workflows using Zaps, Python scripts, and AI tools. Learn to build custom actions, manage data with Zapier Tables and Interfaces, and integrate OpenAI for intelligent automation across real-world business tasks.
The course is a hands-on, in-depth program designed specifically for seasoned programming professionals eager to harness the power of low-code and no-code automation. You’ll start by mastering the fundamentals of Zapier—building and customizing Zaps, Tables, and Interfaces—and quickly move on to authoring bespoke Zap actions in Python. Along the way, you’ll pick up best practices for creating, testing, and debugging Zaps, as well as techniques for persisting data in Zapier Tables and capturing inputs via Zapier Interfaces. We’ll then explore how to seamlessly integrate third-party services and supercharge your automations with OpenAI’s Generative AI models across Zapier’s UI, CLI, and developer platform. You’ll dive into the Platform UI and CLI to develop private, custom Python app integrations, internalize the three core concepts of Zapier integrations, and learn how to embed OpenAI’s AI capabilities within your own Python-based extensions. The course wraps up with concrete, real-world examples that showcase the transformative impact of combining Zapier, Python, and OpenAI in production environments.
32 hours of intensive training with live instruction. This course may be delivered over one or two weeks to accommodate varied scheduling needs.
All students receive comprehensive courseware covering all topics in the course. The instructor distributes courseware via GitHub. The courseware includes documentation and extensive code samples. Students practice the topics covered through challenging hands-on lab exercises. Students will need a free, personal GitHub account to access the courseware. All students will need a modern web browser such as Google Chrome. The instructor will provide Zapier accounts for the students. Student machines will need a text editor, such as VS Code, the latest LTS version of Node.js, and the latest Python version. Students will need permission to install NPM packages and PyPi packages. Preconfigured student virtual machines can provided upon request.