For the online event this September, we have 2 talks lined up, covering topics which should be most relevant for any Machine Learning and Data Engineering practice today.
The first talk by Upkar Lidder will introduce techniques for removing bias and discrimination in the ML pipeline. The second talk by Waad Aljaradt will shed light on data analytics roles and challenges in Facebook.
Should not be missed!
1715-1800: Talk 1 + Q&A
1800-1845: Talk 2 + Q&A
Note: Event link will be made available closer to the date.
Talk 1: Removing Unfair Bias in Machine Learning
Speaker: Upkar Lidder, IBM Data Science & AI Developer Advocate
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 webinar you’ll learn the debiasing techniques that can be implemented by using the open source toolkit AI Fairness 360.
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 bias metrics, bias mitigation algorithms, and metric explainers from the top AI fairness researchers across industry & academia.
In this session you’ll learn:
* How to measure bias in your data sets & models
* How to apply the fairness algorithms to reduce bias
* How to apply a practical use case of bias measurement & mitigation in a data-driven medical care management scenario
Upkar Lidder is a Full Stack Developer and Data Wrangler 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. Upkar went to graduate school in Canada and currently resides in the United States.
Talk 2: Analytics at Facebook
Speaker: Waad Aljaradt, Data Engineer at Facebook
Deep-dive into Facebook Analytics roles, in this session we will learn about Data Engineering and Data Science careers within Facebook and what kind of technical challenges that we are trying to solve as part of Facebook products family. I will share details about the skills that you need to be success-full at these roles and go over real life examples of analytics problems that we are solving to better understand our platform.
The session will be interactive and open to all levels for developers and whoever is interested in Data careers
Waad Aljaradt is a Data Engineer at Facebook. In 2015 she was awarded the Fulbright Scholarship to complete her Master’s degree in Software Engineering with an emphasis on data mining from San Jose State University.
Currently working with the Identity Integrity team within Facebook her role is to contribute to the success of the product with data insights and surface important metrics that can help the team monitor goals and overall product health, by building data pipelines that process raw data and create data sets that powers dashboards and metrics.
She is very involved within the data career community mentoring developers who aim to embark into a data career change, also in 2018 She was a panel speaker at Grace Hopper conference discussing data careers and sharing keys to success in the field.