Register on Tito: https://ti.to/sf-python/nov-2018-presentation-night
On Nov 14, join ~180 devs at SF Python's presentation night & learn more about how you can use Python to find faults in the Earth, build machine learning infrastructure, & more!
If you'd like to present a 5 mins Lightning talk or a 10-15 mins Short Talk at future meetups, please submit your talk ideas [here] (https://goo.gl/forms/THhCxuqAeqA73QF22).
Our generous sponsor Yelp will also provide pizza & drinks for this evening.
##PROGRAM
###Lightning talks
- Where's All the Development? a simple, instructive, take on a git-like VCS, Edmund Huber
- Why we choose Python/Flask to power some of the largest retailers in the world, Morgan Linton
###Short talk(~10 mins + Q&A)
Finding faults with Python, Rob Sare
Speaker Bio
Rob Sare is an Earth observation & Python enthusiast who enjoys working on impactful problems involving spatial & environmental data. He is pursuing a Ph.D. at Stanford, where he uses image processing & numerical modelling techniques to investigate landscapes affected by earthquakes & other tectonic activity at regional & global scales. In his free time, he is an avid cyclist & enjoys contributing to open source projects, & has recently developed tools for classification of rivers in satellite imagery & real-time gas sensor data analysis during internships at Los Alamos National Lab & the U.S. Geological Survey.
Abstract
Most people living in the Bay Area have felt an earthquake & the instrumental record gives us a real-time view of seismic activity today. However, even in areas with sparse historic data, the landscape can give us important clues to how mature or active a fault zone might be in the form of fault scarps & other earthquake-related landforms like those in the famous Olema Valley south of Point Reyes. This talk is about scarplet, a Python image processing package for detecting & measuring key attributes of these & other landforms in topographic data.
###Main talk (~25 mins + Q&A)
LyftLearn - Machine Learning Infrastructure at Lyft, Narek Amirbekian
Speaker Bio
Narek is a software engineer on the machine learning platform team at Lyft. Previously, he's worked at a number of data centric roles at Zendesk, 4INFO, Edmodo, & Aarki.
Abstract
At Lyft we have hundreds of models running at any given time. In the past every team had to build custom infrastructure to prototype, train, & deploy their own models. Now we have a shared platform where Data Scientists can prototype their models, save them, schedule regular retraining, & even deploy the to run in production. The backend is written on python running on kubernetes.
##AGENDA
6:00p - Check-in & mingle, with food provided by our generous sponsor!
7:05p - Welcome
7:30p - Door close
7:10p - Announcements, lightning talks & main talk
8:15p - More mingling
9:30p - Hard stop
*SF Python is run by volunteers aiming to foster the Python community in the Bay Area. Please consider making a donation to SF Python & saying a big thank you to Yelp for providing pizza, beer, & the venue for this Wednesday's meetup.*
*Yelp sees 89 million mobile users & 79 million desktop users every month. Keeping everything running smoothly requires the best & brightest in the industry. Their engineers come from diverse technical backgrounds & value digital craftsmanship, open-source, & creative problem-solving. They write tests, review code, & push multiple times a day. Come out & talk to them.*
