Getting Started with MLflow
Efficiently delivering machine learning products is not easy, therefore good tools that support ML model development are needed. This course will teach you MLflow.
What you'll learn
Developing machine learning models in teams, with real-world data and serving real-world business needs may be complex. In this course, Getting Started with MLflow, you’ll learn to manage the full lifecycle of machine learning models. First, you’ll explore how to track your machine learning experiments for easy comparison and reproducibility. Next, you’ll discover ways of using MLflow to collaborate on model development in teams of any size. Finally, you’ll learn how to share your models in a way that makes them ready for use in real products. When you’re finished with this course, you’ll have the skills and knowledge of MLflow needed to create machine learning models in a collaborative, reproducible, and production-ready way.
Table of contents
About the author
Paweł is a software engineer passionate about knowledge sharing. He's especially focused on processing and exploring data sets (be it big or small) and is always searching for emerging tools that will make working with data simpler in the future. Paweł is currently at Farfetch where he develops data applications and sees his favorite tools like Pandas, Kafka or ElasticSearch (just to name a few) being applied to solve complex business problems. He mostly works with JVM languages and Python, but ... more