BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20200308T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20191103T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260409T015751Z UID:iqOWPA DTSTART;VALUE=DATE:20200212 DTEND;VALUE=DATE:20200213 CLASS:PUBLIC CREATED:20200105T162403 DESCRIPTION: On Wed\, Feb 12\, join ~180 devs at SF Python’s presentation night. \n\n 🖋Register here to attend: https://ti.to/sf-python/hypothes is-assumptions-and-bias \n\n 🔥 [NEW] Want do to improve the content at SF Python? Please fill out our survey here \n\n Our generous sponsor Yelp will provide pizza and drinks. \n\n PROGRAM \n\n Lightning talks \n\n \n E nabling Fastai Multi-GPU/DDP Training in Jupyter Notebook - Phillip Chu \n Python's best AI package - Cameron Smith \n Unittest AsyncMock - Pranay S uresh \n \n\n Short talk(~10 mins + Q&\;A) \n\n In-Database Machine Lea rning w PostgreSQL - Nitin Borwankar \n\n Machine learning pipelines invol ve multiple transformation steps very often involving cleaning and moving massive amounts of data. In-database ML is a single stack ML approach wher e all pipeline steps happen inside the database\, minimizing massive data movement. PostgreSQL is the only open source database that supports this p aradigm.\nThis talk describes how this process works with description of a rchitecture\, internals and live demo with well known data sets. We also t ouch upon additional Postgres features that support a NoETL paradigm\, red ucing end to end time and resources even further. \n\n Bio \n\n Nitin Borw ankar has been a user of Python since the days of Zope and has played almo st every role in the software development lifecycle over 25 years from QA Engineer\, to App developer\, Database Architect\, Product Manager\, Engin eering Manager and Data Scientist. He is a founder and CTO of Numericc. \n \n Main talk (25 mins+Q&\;A) \n\n Removing Unfair Bias in Machine Learn ing - Upkar Lidder \n\n Extensive evidence has shown that AI can embed hum an 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 &\; di scrimination in the machine learning pipeline? In this talk you'll learn t he de-biasing techniques that can be implemented by using the open source toolkit AI Fairness 360. \n\n AI Fairness 360 (AIF360) is an extensible\, open source toolkit for measuring\, understanding\, and removing AI bias. AIF360 is the first solution that brings together the most widely used bia s metrics\, bias mitigation algorithms\, and metric explainers from the to p AI fairness researchers across industry &\; academia. \n\n In this ta lk we’ll cover: \n\n · How to measure bias in your data sets &\; mod els\n· How to apply the fairness algorithms to reduce bias \n\n What you' ll learn: \n\n An introductory look at how bias &\; discrimination can arise within modern machine learning techniques and the methods that can b e implemented to tackle those challenges. Learn how to evaluate the metric s using the open-source AI Fairness 360 Toolkit to check for fairness and mitigate machine learning model bias. \n\n Bio \n\n Upkar Lidder is a Full Stack Developer and Data Wrangler at IBM with a decade of development exp erience in a variety of roles. He can be seen speaking at various conferen ces and participating in local tech groups and meetups. He is currently cu rious about magic behind Machine Learning and Deep Learning. Upkar went to graduate school in Canada and currently resides in the United States. \n\ n 🖋Register here to attend: https://ti.to/sf-python/hypothesis-assumpti ons-and-bias \n\n AGENDA \n\n 6:00p - Check-in and mingle\, with food prov ided by our generous sponsor! \n\n 7:05p - Welcome \n\n 7:30p - Door close \n\n 7:10p - Announcements\, lightning talks and main talk \n\n 8:30p - S urprise and more mingling \n\n 9:00p - Hard stop \n\n This event is produc ed by: SF Python \n\n 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 PyBay\, Python conference in SF every August. \n\n Fo od and Venue is from: Yelp \n\n Yelp sees 89 million mobile users and 79 m illion desktop users every month. Keeping everything running smoothly requ ires the best and brightest in the industry. Their engineers come from div erse technical backgrounds and value digital craftsmanship\, open-source\, and creative problem-solving. They write tests\, review code\, and push m ultiple times a day. Come out and talk to them. \n\n Video production is f rom: Sauce Labs \n\n Sauce Labs ensures the world’s leading apps and web sites work flawlessly on every browser\, OS and device. Their award-winnin g Continuous Testing Cloud provides development and quality teams with ins tant access to the test coverage\, scalability\, and analytics they need t o rapidly deliver a flawless digital experience. \n LAST-MODIFIED:20240728T211358 LOCATION:140 New Montgomery · San Francisco\, CA ORGANIZER:mailto:grace@pybay.com SUMMARY:Data Fans: Learn about Hypothesis\, assumptions\, and bias in softw are URL;VALUE=URI:https://ti.to/sf-python/hypothesis-assumptions-and-bias URL;VALUE=URI:https://ti.to/sf-python/hypothesis-assumptions-and-bias END:VEVENT END:VCALENDAR