Exploring XMPro Notebook and MLflow for Data Science and Model Governance

This video provides a technical walkthrough of the new XMPro Notebook and its applications in data science, scientific computing, and machine learning. We also examine the utility of MLflow Agent for effective model governance within an ML Ops framework.

Learning Objectives:

Understand how XMPro Notebook provides an interface compatible with Jupyter for data science. Familiarize yourself with the built-in ChatGPT for code generation and troubleshooting. Learn how to execute and store models using the Python Agent and MLflow Library. Gain insight into the importance of an organized ML Ops framework for model governance. Observe a live demonstration of MLflow Agent managing model versions seamlessly.

MORE INFO ON HOW TO CREATE INTELLIGENT DIGITAL TWINS USING XMPRO AI : https://www.youtube.com/watch?v=li_EXCTmVOQ

Transcript

this is an embedded Jupiter notebook

providing a familiar interface for data

science scientific Computing and machine

learning

data scientists analysts and Engineers

will be able to access data to innovate

within the XM Pro Suite

we've added built-in chat GPT

functionality to help you in the process

for example you can ask it to create

code to represent data in a certain

visualization

[Music]

when you're done you can save the file

and execute it using our python agent as

embedded AI

or you can apply governance

leverage the mlflow library to commit

the model to your repository right from

within X in Pro notebook

for use in a data stream

this was just a small test of XM Pro

notebook

in last month's webinar Gavin Green

presented a Hands-On demonstration of AI

in intelligent digital twins which is a

fully extended version that explains

these features and more in detail it is

well worth watching