Scale-up your models development with MLflow
Data Science
Experimenting with a model or comparing a model's performance with other solutions is often a tricky task. Problems can include absence of an intuitive graphical interface and lack of an SDK that allows this work to be done neatly.
MLflow is a perfect solution. MLflow is an open-source project that covers all the aspects of an ML workflow, from model exploration to production and retirement. Easily integrated into any ML library, it can be run on the cloud and it allows an easy cross-team collaboration. For these reasons, MLflow has been widely used across industries, from Booking.com to Microsoft and Facebook.
In this talk, Stefano Bosisio will explore MLflow Tracking. Tracking allows data scientists to have an intuitive graphical interface to understand and immediately visualise their models' experiments performance and metrics. We will go through a Python SDK implementation of MLflow, to show not only how MLflow is easy to be integrated with custom-based products but also to give data scientists an easy interface to immediately experiment with their models.
