Data Fans: learn more about hypothesis, assumptions, and bias in software

Details

On Wed, Feb 12, join ~180 devs at SF Python’s presentation night.

🖋Register here to attend: https://ti.to/sf-python/hypothesis-assumptions-and-bias

Our generous sponsor Yelp will provide pizza and drinks.

PROGRAM

Lightning talks
Enabling Fastai Multi-GPU/DDP Training in Jupyter Notebook - Phillip Chu
Python's best AI package - Cameron Smith
Discovering Hypothesis - Yann Kaiser

Short talk(10 mins+Q&A)
In-Database Machine Learning w PostgreSQL - Nitin Borwankar

Machine learning pipelines involve multiple transformation steps very often involving cleaning and moving massive amounts of data. In-database ML is a single stack ML approach where all pipeline steps happen inside the database, minimizing massive data movement. PostgreSQL is the only open source database that supports this paradigm.

This talk describes how this process works with description of architecture, internals and live demo with well known data sets. We also touch upon additional Postgres features that support a NoETL paradigm, reducing end to end time and resources even further.

Bio

Nitin Borwankar has been a user of Python since the days of Zope and has played almost every role in the software development lifecycle over 25 years from QA Engineer, to App developer, Database Architect, Product Manager, Engineering Manager and Data Scientist. He is a founder and CTO of Numericc.

Main talk (25 mins+Q&A)
Removing Unfair Bias in Machine Learning - Upkar Lidder

Extensive evidence has shown that AI can embed human and societal bias and deploy them at scale. And many algorithms are now being reexamined due to illegal bias. So how do you remove bias & discrimination in the machine learning pipeline?

In this talk you'll learn the de-biasing techniques that can be implemented by using the open source toolkit AI Fairness 360. This toolkit is the first solution that brings together the most widely used bias metrics, bias mitigation algorithms, and metric explainers from the top AI fairness researchers across industry & academia. You'll take an introductory look at how bias & discrimination can arise within modern machine learning techniques and the methods that can be implemented to tackle those challenges. Learn how to evaluate the metrics using the open-source AI Fairness 360 Toolkit to check for fairness and mitigate machine learning model bias.

Bio

Upkar Lidder is a Full Stack Developer and Data Wrangler at IBM with a decade of development experience in a variety of roles. He can be seen speaking at various conferences and participating in local tech groups and meetups. He is currently curious about magic behind Machine Learning and Deep Learning. Upkar went to graduate school in Canada and currently resides in the United States.

🖋Register here to attend: https://ti.to/sf-python/hypothesis-assumptions-and-bias

AGENDA
6:00p - Check-in and mingle, with food provided by Yelp!
7:05p - Welcome
7:30p - Door close
7:10p - Announcements, lightning talks and main talk
8:30p - Surprise and more mingling
9:00p - Hard stop

This event is produced by:

SF Python is a volunteers-run organization aiming to foster the Python community and ecosystem in the Bay Area. They produce ~20 events a year and https://pybay.com, Python conference in SF in August.

Food and Venue:

Yelp sees 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.

Video production:

Sauce Labs ensures the world’s leading apps and websites work flawlessly on every browser, OS and device. Their award-winning Continuous Testing Cloud provides development and quality teams with instant access to the test coverage, scalability, and analytics they need to rapidly deliver a flawless digital experience.