What we're about

"Learn by Practicing". Join us to learn and practice AI, Machine learning, Deep learning and Data Science technology together with like-minded developers.

Our goal is to congregate with AI enthusiasts from all over L.A to learn and practice AI tech, through tech talks, study jams, code labs etc.. we regularly invite tech leads from innovated companies, successful startups to share their practice experiences and practices in the world of AI, Cloud, Data, Blockchain.

If you’d like to speak at future meetups, co-promote your meetup or inquire about partnership opportunities, please feel free to reach out to us.

Due to the Covid-19, we have moved all our meetup events online: https://www.aicamp.ai/event/events

Upcoming events (2)

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

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This is In-person + virtual event, please register on the event website to receive joining link:

* 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

ML Talk: Navigating the Full Stack of Machine Learning

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Please complete registration on eventbrite to receive join link: http://bit.ly/3ukuvp3

Ethan Rosenthal is a data scientist at Square, has worked as a data consultant, and used to be a scientist scientist with a PhD in Physics from Columbia University. In this fireside chat, Ethan joins Hugo Bowne-Anderson, Outerbounds’ Head of Developer Relations, to discuss the wild west of full stack machine learning and how to make sense of all the feature stores, metric layers, model monitoring, and more with a view to deciphering what mental models, tools, and abstraction layers are most helpful in delivering actual ROI using ML.

After attending, you’ll know

  • How to think about the full stack of machine learning in a principled way;
  • What the most important layers in the ML stack are for data scientists;
  • How to separate the wheat from the chaff when thinking about which tools and abstraction layers to adopt for your team;

Past events (350)

Photos (318)