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ML Monthly Meetup: Realtime Machine Learning and End-to-end ML Platform

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ML Monthly Meetup: Realtime Machine Learning and End-to-end ML Platform

Details

This is In-person + virtual event, please register on the event website:
https://www.aicamp.ai/event/eventdetails/W2022120618

[Important update]

  • Attendees are required to register at the event website. (Correct name is required for printing badge and check in. NO walk-ins, NO access without badge)
  • To attend remotely, you also are required to register at the event website to receive your customized joining link.

Agenda:
* 6:00pm~6:30pm: Checkin, Food and Networking (In-person)
* 6:30pm~8:00pm: Tech talks (In-person + Virtual)
* 8:00pm~8:30pm: Lucky Draw & Networking (In-person)

Tech Talk 1: Seven Reasons Why Realtime ML Is Here to Stay.
Abstract: Machine Learning is being adopted and validated by an increasing number of industries, businesses, and projects. However, a significant portion of these use cases are offline and batch in nature. The transition to realtime predictions is a rapidly evolving and widespread trend right now. In this talk, we'll look at some of the top reasons why machine learning is quickly transitioning to realtime.
Speaker: Nikhil Garg, CEO of Fennel.

Tech Talk 2: Looper: The end-to-end ML platform at Meta
Abstract: Modern software systems and products increasingly rely on machine learning models to make data-driven decisions based on interactions with users, infrastructure and other systems. For broader adoption, this practice must (i) accommodate product engineers without ML backgrounds, (ii) support fine grain product-metric evaluation and (iii) optimize for product goals. To address shortcomings of prior platforms, we introduce general principles for and the architecture of an ML platform, Looper, with simple APIs for decision-making and feedback collection. Looper covers the end-to-end ML lifecycle from collecting training data and model training to deployment and inference, and extends support to personalization, causal evaluation with heterogenous treatment effects, and Bayesian tuning for product goals.
Speaker: Igor Markov, Research Scientist, AI platforms, Meta

SF/Bay AI/ML/Data Developers Group
SF/Bay AI/ML/Data Developers Group
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