ML in Practice
Details
External registration required at nyhackr.org.
We're starting 2024 with a talk about implementing machine learning.
Thank you to NYU for hosting us.
Everybody attending must RSVP through the registration form at nyhackr.org. There is a charge for in-person and virtual tickets are free. Space is limited and in-person registration closes at 2 PM January 23rd.
About the Talk:
AI/ML is all everyone wants to talk about these days and build applications off of. However when it comes to describing the what and how of ML systems, we appear to have a disembodied perspective of a black box. In this talk, running around 35 minutes, I describe a few accompanying perspective traps that lead to such mental models. I go on to show how ML models are interconnected with software applications, some of the hard engineering designs and consequent technical debt. In short, a preview into ML in practice and the engineering considerations and labor that go into building useful ML systems. This talk will be helpful for folks attempting to build/learn about practical data hygienic ML systems.
About Vikram:
I am a Sr. ML engineer with experience in ranking and recommendation algorithms, segmentation techniques, natural language processing (NLP), and heterogeneous treatment effect/causal modeling within large communities/social networks at Meta. I am experienced in research areas such as Bayesian neural networks, and causal inference, with explorations of reinforcement learning techniques. I am a published author in niche technology areas, with a strong background in mentoring and team building. I am a regular speaker on industrial use of machine learning and practical algorithmic techniques at academia. In the past, I have led teams of up to 25 engineers in globally distributed work setups.
The venue doors open at 5:30 PM America/New_York where we will continue enjoying pizza together (we encourage the virtual audience to have pizza as well). The talk, and livestream, begins at 6:00 PM America/New_York.
Remember, register at nyhackr.org.
COVID-19 safety measures
ML in Practice