[Baypiggies] This month's meeting: Data Science Night on Thursday November 16

[Jeff Fischer]

Due to the Thanksgiving holiday, we will have our November meeting a week
earlier than usual, on Thursday November 16. This month, we will have two
exciting talks on data science / data engineering topics.

*Location*
We will be in a different building from last month, just down the
street: LinkedIn,
Unify Meeting Room 950 W. Maude Ave, Sunnyvale
<https://goo.gl/maps/sZT4EKDPt762>.

*RSVP*
Please RSVP on our meetup.com page:
https://www.meetup.com/BAyPIGgies/events/242072331/

*Talk 1: Green circles for success: hands-off ETL using Airflow with unit
tests*
*Speaker: *Sam Zeitlin
*Abstract*
My team uses Airflow to run daily and hourly jobs that parse logs and
transfer data into AWS Redshift for easier access. I’ll talk about why
Airflow is better than cron jobs: it’s python, and keeps track of which
tasks succeeded and which failed, so you don’t have to restart from scratch
if anything goes wrong.

I’ll also talk about what can go wrong with Airflow jobs, and how I came up
with reusable templates for regression tests to support creating new jobs,
and upgrading from an older version to a newer version.

*Author Bio*
I’m a former research scientist, self-taught Pythonista, and my current job
title is Product Hacker at Oath, Inc. My team does prototyping for our
DevOps, Engineering, and Product teams. What that usually means is a mix of
data engineering (we often build our own data pipelines), data science, and
product management.

*Talk 2: Python Packaging for Machine Learning*
*Speaker:* Steven Cutting
*Abstract*
Do you have machine learning models written in Python that you would like
to share with other people (e.g. clients, co-workers, etc.)? This talk will
show you how easy it can be to create our own pip installable packages and
extend the reach of your projects.

In this talk we will learn how to make a simple machine learning solution,
written in Python, pip installable. Thus any users of our model who know
how to use pip will have a much easier time getting it and its dependencies
installed. The example machine learning solution that we will use provides
a Python API so that it can be used in other Python programs. It also
provides scripts that will be made available to the user when the package
is installed using pip. We will cover how to package not only the code but
a fitted model as well.

*Author Bio*
I have worked as a consultant for the past 3 years. In some projects, I
created machine learning solutions that needed to be incorporated into
production applications that either I or someone else had written. As a
result I have developed experience packaging machine learning projects to
make them easier to share and deploy.


*Meeting Schedule:*
7:00 pm Food and Announcements
7:15 pm Talks start
8:30 pm Networking
9:00 pm Event ends
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/baypiggies/attachments/20171106/ba362070/attachment.html>