A team of core committers explain what is coming to Airflow 2.0.

This talk discusses how to build an Airflow based data platform that can take advantage of popular ML tools (Jupyter, Tensorflow, Spark) while creating an easy-to-manage/monitor As the field of data science grows in popularity, companies find themselves in need of a single common language that can connect their data science teams and data infrastructure teams. Data scientists want rapid iteration, infrastructure engineers want monitoring and security controls, and product owners want their solutions deployed in time for quarterly reports. This talk will discuss how to build an Airflow based data platform that can take advantage of popular ML tools (Jupyter, Tensorflow, Spark) while creating an easy-to-manage/monitor ecosystem for data infrastructure and support team. In this talk, we will take an idea from a single-machine Jupyter Notebook to a cross-service Spark + Tensorflow pipeline, to a canary tested, production-ready model served on Google Cloud Functions. We will show how Apache Airflow can connect all layers of a data team to deliver rapid results.