Abstract: 

Kedro is a development workflow tool open sourced by QuantumBlack, a McKinsey company. Many data science teams have started using the library for their pipelines but are unsure how to integrate with other model tracking tools, such as MLflow. In this tutorial, we will give an overview of Kedro and MLflow and demo how to leverage the best of both.

The goal of this session is to demonstrate how Kedro and MLflow fit together in a scalable AI architecture. To start, we will give an overview of Kedro and an overview of MLflow: - What are they used for? - What functionality do they provide? - How do they compare as tools?

Next, we will walk through a demo of a Kedro project that has MLflow integrated into it. Finally, we will go over deployment options.

There will be time allocated at the end for Q&A.

● Kedro;
● MLflow;

https://github.com/tgoldenberg/kedro-mlflow-example
https://drive.google.com/open?id=16-NNCcGZnZv-asahJD9czz3SeOjtDFNX

Bio: 

Tom is a Junior Principal Data Engineer at QuantumBlack, a McKinsey Company. Prior to consulting, Tom was CTO and co-founder of Commandiv, a wealth management startup.