As a data scientist, I’ve learned two things in the past few years. When it comes to making decisions about machine learning (ML) models, you need to gather the right evidence regarding your results, and communicate it to your project team and stakeholders. In other words, you can’t put models in production if you don’t look at the right things and don’t do it collectively.
In this blog post series, you’ll learn how to do that by building and monitoring ML models in Watson Studio (IBM Cloud Pak for Data’s set of Data Science services) and by visualizing and interacting with these models from Streamlit:
Can’t wait to get started? Explore the full code used for this post here.