Python + MPP Database = Large Scale AI/ML Projects in Production Faster

Getting Python data science work into large scale production at companies like Uber, Twitter or Etsy requires a whole new level of data engineering. Economies of scale, concurrency, data manipulation and performance are the bread and butter of MPP analytics databases. Learn how to take advantage of MPP scalability and performance to get your Python work into production where it can make an impact.
Who is this presentation for?
Analytics architects, directors of data science projects, analytics application developers, anyone who wants to get their Python work into large scale production
Prerequisite knowledge:
Basic understanding of Python and data analysis work Some knowledge of data engineering at scale preferred
What you'll learn?
Deepen understanding of data engineering requirements of scaling a prototype to full production levels, MPP analytics database principles, Introduction to working with MPP databases using Python, some information on what NOT to do when developing Python work to make it easier to put into production.

In 23 years in the data management industry, Paige Roberts has worked as an engineer, a trainer, a support technician, a technical writer, a marketer, a product manager, and a consultant. She has built data engineering pipelines and architectures, documented and tested open source analytics implementations, spun up Hadoop clusters, picked the brains of stars in data analytics and engineering, worked with a lot of different industries, and questioned a lot of assumptions. Now, she promotes understanding of Vertica, MPP data processing, open source, high scale data engineering, and how the analytics revolution is changing the world.