I’ve always been amazed by how programming extends human capabilities, which was part of what inspired me to pursue computer science. When used carefully and responsibly, machine learning can enable humans even further, amplifying what we can do in fascinating ways.
I was excited to talk at our Virtual Global Inspire conference about automated machine learning and EvalML, an Alteryx Open Source Python library that makes it easy to create, understand and debug ML models from a variety of sources.
Whether you want to build a model to better understand how a system works or you want to generate accurate predictions, EvalML can guide you to a solution efficiently. Using best practices for machine learning, EvalML can help even people who aren’t experts obtain quality results, reducing time and effort spent on tuning and evaluating models.
Check out the Inspire video (accessible in the Alteryx Community blog post below) to learn more about automated machine learning and EvalML. I also demo using EvalML to build a model to predict fraudulent credit card transactions.
dylanjsherry
Automated approaches make machine learning easier, but not everyone wants to dive into coding. If this seems daunting, take a look at the Alteryx Intelligence Suite and Alteryx Machine Learning, which also offer tools for quickly and efficiently building models.
If you’d like to see a couple of additional demos of EvalML, check out our videos showing how to use it for predicting housing prices and customer churn.
Thanks for stopping by to learn more about EvalML. I’m excited to see what you’ll do with it and how we can all make the most of automated machine learning’s potential.
Recommended resources
- Exploring EvalML: Automatically Build, Optimize and Evaluate Models
- Easy, Open-Source AutoML in Python with EvalML
- Automodeling with EvalML: Bank Customer Churn (video demo)
- An autoML approach to predicting housing prices with EvalML (video demo)
- Using Text Data in EvalML with Woodwork
- EvalML on GitHub
