How to train, evaluate, and deploy a machine learning workflow with tidymodels & Posit Team

Simon Couch from the tidyverse team at Posit joined us to walk through an end-to-end machine learning workflow with Posit Team. 

Machine learning models are all around us, from Netflix movie recommendations to Zillow property value estimates to email spam filters.

As these models play an increasingly large role in our personal and professional lives, understanding and embracing them has never been more important; machine learning helps us make better, data-driven decisions.

The tidymodels framework is a powerful set of tools for building—and getting value out of—machine learning models with R.

Data scientists use tidymodels to:

1. Gain access to a wide variety of machine learning methods
2. Guard against common mistakes
3. Easily deploy models through tidymodels’ integration with vetiver

Helpful resources:

• GitHub Repo
• Q&A Recording
• Blog post on tidymodels + Posit Connect
• Tidy Modeling with R book
• Posit Team
• Request evaluation
• Subscribe to learn more about Posit events

Timestamps:

1:44 – Three steps for developing a machine learning model

3:35 – What is a machine learning model?

7:02 – Overview of machine learning with Posit Team

7:36: Step 1: Understand and clean data

11:05 – Step 2: Train and evaluate models (why you might be interested using tidymodels)

23:02 – Step 3: Deploying a machine learning model from Posit Workbench to Posit Connect

30:14 – Summary

31:21 – Helpful resources