Learn about Machine Learning and Data Pipelines
On Wed Nov 13, join ~180 devs at SF Python's presentation night to learn more about Machine learning and Data pipelines!
Our generous sponsor Yelp will also provide pizza and drinks for this evening.
- Python's Best AI Packages - Cameron Smith
- Streamlit - app framework for ML & Data Science teams - Amanda Kelly
- Team skills and mentoring mentors - Yarko Tymciurak
Please submit your talk ideas 👉 here
Short talk(~10 mins + Q&A)
1 - Taking Python Serverless: Using Zappa to deploy webapps without infrastructure
So you've built your Python application and want to deploy it to your users. How do you go about that? One increasingly common answer to that question is ""deploy it as a serverless application"". What does it mean to deploy a serverless app, and how can you try out serverless in a low-risk way?
This talk will answer both of those questions. First, it will give an overview of serverless applications and the advantages they provide over other deployment modes. Next it will dig into Zappa, a library for deploying serverless python apps, and show how Zappa allows you to deploy your webapp as a serverless application in seconds. This ease also comes with trade-offs, so the talk will close by discussing what you lose with Zappa and how we've worked around those limits at Nuna. By the end of this talk, you'll have a better understanding of serverless applications and you'll be able to evaluate if Zappa is the right fit for your needs.
Jonathan is a software engineer building web applications which get Americans better access to healthcare at Nuna inc. Jonathan spends his day writing Python in django, and his nights learning Arabic. You can find him on the twitterverse at @maltzj.
What's new in Tensorflow 2.0 - Francesco Mosconi
Tensorflow 2.0 was recently announced by Google and it comes with quite a few disruptive changes with respect to both Tensorflow 1.x and Keras. In this 10 minutes talk I will guide you through this changes with example code and explain when and how you should use it to build your AI projects with Python.
Main talk (30 mins)
How Do Algorithms Become Biased? - Eva Sasson
There’s bias in algorithms - how does this happen? In this talk, we will walk through the steps of how to build an algorithm to predict property prices from a dataset of property listings, focusing predominantly on finding the right features to include in building the model. Then, we will understand where in the feature engineering process we start introducing bias into our algorithms, and what are the ramifications of this if the model were to be deployed in the real world. Using the prediction algorithm as a framework to look at each step of the building process, we will also look at real-world examples of when certain decisions have led to unequal and biased results.
Eva Sasson is a technical Product Marketer at Sentry.io. She has presented Network Graphs at the Sunbelt Conference in Utrecht, Netherlands, Pycon Canada, PyTennessee and about Machine Learning Bias at DataDay Mexico. Her passion is to support women and underrepresented communities in tech, in addition to transitioning to a zero waste lifestyle and keeping lots of things in jars.
6:00p - Check-in and mingle, with food provided by our generous sponsor!
7:05p - Welcome
7:30p - Door close
7:10p - Announcements, lightning talks and main talk
8:15p - More mingling
9:30p - Hard stop
This event is produced by:
SF Python, a volunteers-run organization aiming to foster the Python Community in the Bay Area.
Venue and food is donated by:
Yelp. They power 89 million mobile users and 79 million desktop users every month. Keeping everything running smoothly requires the best and brightest in the industry. Their engineers come from diverse technical backgrounds and value digital craftsmanship, open-source, and creative problem-solving. They write tests, review code, and push multiple times a day. Come out and talk to them.