Data Science needs DAGs too - How to use Airflow for ML research and development

Think Airflow isn't useful for machine learning R&D? Think again! Please join us for this talk on how one Data Scientist is leveraging Airflow to make his model research and development experience easier and more productive.

Meet-up topic details:
So far we have mostly talked about airflow in a production setting, covering how data engineers use airflow for data on-boarding and running production models. In this talk, we will be covering how data scientists can use airflow for model research and development and how data engineers can work with data scientists to assist them in the R&D process.

Unlike a production setting, the R&D workflow is less well defined, requires trial and error and frequent resetting of tasks and output as data, models and parameters change. It's less about recovering from failures and making sure everything is running smoothly and more about generating insights and assessing predictive power with different models and parameters.

We will be using the d6tflow (https://github.com/d6t/d6tflow) open source python library to bring airflow-style DAGs to the data science research and development process. We will show you how to make building complex data science workflows easy, fast and intuitive.

Presenter:
Norman Niemer - Chief Data Scientist at a large asset manager where he delivers data-driven investment insights. He holds a MS Financial Engineering from Columbia University and a BS in Banking and Finance from Cass Business School (London).
https://www.linkedin.com/in/normanniemer/

Pizza, drinks and mingling will follow the presentation.

Instructions to follow upon arrival:
Enter the lobby on the north side of the building. A representative will be waiting next to one of north end elevator turnstiles with a sign that says 'Airflow Meet-Up'. They will assist you in getting through security and send you up to the 15th floor where another representative will be waiting to direct you to the room.