Discover how you can navigate the expansive world of Machine Learning Operations and learn the basics of MLOps in this new data demo!
MLOps is a collection of best practices for putting machine learning models into production and then maintaining those models in production. In this comprehensive data demo, you’ll learn how MLOps spans the machine learning lifecycle from data preparation, through model training and tuning, and improving deployed models.
Instructor Brian Spiering will guide you through how to use Python’s MLflow package to track model experiments and set up models to deploy into production.
Github Library (to follow along!)
- Learn the basics of MLOps and its capabilities
- Create machine learning experiments in Python’s MLflow
- Package trained machine learning model for production with MLflow
About our Instructor:
Brian studied psychology and neuroscience at the University of California, Santa Barbara where he became fascinated with coding statistical software and data infrastructure. He then transitioned into tech, focusing on data engineering, natural language processing (NLP), and teaching. In his spare time, he enjoys camping, rock climbing, and playing guitar.