Name: Putting Machine Learning Models into Production and an Introduction to JuliaDB
Start: 2018-04-26T18:30:00-07:00
End: 2018-04-26T21:00:00-07:00
Location: DataScience.com

Dear PyData enthusiasts,

We are excited to announce the speaker lineup for our next PyData SoCal meetup! Simon Byrne from Julia Computing and Irina Kukuyeva from Dia&Co will be joining us on April 26th for a conversation on deploying machine learning in production and JuliaDB.

Agenda:
6:30 pm - 7:00 pm - Arrivals, eat/drink and network
7:00 pm - 7:45 pm - "Productionalizing Machine Learning Models: The Good, The Bad and The Ugly" by Irina Kukuyeva
7:45 pm - 8:30 pm - "JuliaDB: high-performance data analysis" by Simon Byrne
8:30 pm - 9:00 pm - Networking

Abstracts and Bios:

"Productionalizing Machine Learning Models: The Good, The Bad and The Ugly" - Irina Kukuyeva (Dia&Co)

Abstract:
Data science teams tend to put a lot of thought into developing a predictive model to address a business need, tackling data processing, model development, training, and validation. After validation, the model then tends to get rolled out -- without much testing -- into production. While software engineering best practices have been around for a long time until recently, no formal guidelines existed for checking the quality of code of a machine learning pipeline.

The talk will cover tips and best practices for writing more robust production-ready predictive model pipelines. We know that code is never perfect; Irina will also share the pains and lessons learned from experience productionizing and maintaining 4 customer-facing models at 4 different companies: in online advertising, consulting, finance, and fashion.

"JuliaDB: high-performance data analysis" - Simon Byrne (Julia Computing)

Abstract:
Julia is a dynamic high-performance language, particularly aimed at technical computing. Though its primary focus so far has been traditional "number crunching" work, we are also in the process of building JuliaDB, a fast, scalable analytic database, written entirely in Julia. In this talk Simon will give an introduction to Julia and JuliaDB, demonstrating the tools and features of both the language and the database

Bios:

Irina Kukuyeva has been a Data Scientist for over 10 years, developing predictive models for start-ups as well as mid- and large-sized companies in areas of fashion, IoT, healthcare, market research, and online advertising. She is a Sr. Data Scientist and ML Engineer at Dia&Co. In her spare time, she enjoys mentoring adults, that are starting out in tech, as well as early-stage start-ups.

Simon Byrne is the Director of Solutions at Julia Computing, where he implements cutting-edge quantitative routines for data manipulation, statistics and machine learning. Simon has a Ph.D. in mathematics from the University of Cambridge and has extensive experience in computational statistics and machine learning in both academia and industry. He has been contributing to the Julia project since 2012.

Parking & Other Info:
Enter from Hannum Ave into the parking structure for the 200-300 buildings • Once you park, exit the parking structure on P4 near the elevators.

Head across the courtyard veering slightly to the right and you will see the 200 building. If you find the 300 or 100 buildings you are in the wrong place. Take the elevator to the second floor.

Parking must be validated, please bring your ticket to the meetup.

Pramit Choudhary

Dave

Jean-Rene Gauthier

PyData SoCal

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Putting Machine Learning Models into Production and an Introduction to JuliaDB

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