What we're about

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

  • Update (September 2022), we start to resume in-person meetups. you can join us: submit topics (online or in-person), sponsor the venue/food, or join community leadership team to manage and grow the local community.
  • Update (March 2020): Due to the Covid-19, we have moved all our meetup events online: https://www.aicamp.ai

AICamp is a large AI/ML/Data developers community globally, with 180K+ developers from 150+ countries, we also have 40+ local study groups in 15 countries. Our mission is to enable every developer to learn and practice AI/ML/Data technology from anywhere at any time. We host many online tech events at the daily basis, such as webinars, workshops, tutorials, bootcamps, and large tech summit/conference.
Contact us (info AT aicamp DOT ai) if you are interested in speaking your topics, promoting your events, collaboration with your events, or volunteering to build and grow the local AI/ML/Data community.
** For speaking, submit your topics:
-- Topic and Abstract: the topic needs to be technology focused, generally good for engineers, developers to learn and practice on AI, Machine Learning, Deep Learning, Data Science, Cloud, etc..
-- Speaker: bio, linkedin profile and/or past speaking experiences.
-- Which cities (regions) you want to speak to (check the list of cities on the website). 
--Event type: webinar (1hour), hands-on workshop (2-3 hours), bootcamp (5-8 hours), or course (8+ hours)
-- Preferred dates: 
-- Will your company sponsor the talk?

** For promoting events, we have promotional packages to help scale your great events to the city/regional/global communities. contact us for the prospectus and many recent user cases.

-- For sharing job openings, you can share to the community discussion group on slack (4000+ members, join slack link: https://bit.ly/3iLe40y ) or sponsor one of our events to announce to attendees. or even better, we can host tech recruiting events to attract more talents. contact us for details.

** For partnering events. we help many tech organizations, meetup groups, communities to run their online events. contact us for details how we can collaborate.

Contact: support@aicamp.ai
*AI online learning/practicing platform: https://www.aicamp.ai
*AI online community platform: https://www.aicamp.ai/event/events
*50+ Local AI developers communities around world: https://www.aicamp.ai/index/localchapter

Upcoming events (3)

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

Z-Park Silicon Valley Innovation Center

This is In-person + virtual event, please register on the event website:

[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.

* 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

Link visible for attendees

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;

ML Monthly Meetup: Causal Inference and Feature Engineering

Z-Park Silicon Valley Innovation Center

You can submit topics to speak to the community. We also looking for sponsors to support venues, food/refreshment.

* 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: Causal Inference - 4 Foundational Methods
Abstract: Measuring causal effects from observational data is asked more and more, but it is hard to generate robust measurements without knowing the key challenges of observational data and potential solutions. This talk presents (1) differences between prediction vs. causal inference, (2) key challenges of causal inference from observational data, and (3) 4 foundational causal inference methods.
Speaker: Minha Hwang, Principal Architect @ Microsoft

Tech Talk 2: Scalable Feature Engineering with Hamilton
Abstract: In this talk we present Hamilton, a novel open-source framework for developing and maintaining scalable feature engineering dataflows. Hamilton was initially built to solve the problem of managing a codebase of transforms on pandas dataframes, enabling a data science team to scale the capabilities they offer with the complexity of their business. Since then, it has grown into a general-purpose tool for writing and maintaining dataflows in python. We introduce the framework, discuss its motivations and initial successes at Stitch Fix, and share recent extensions that provide runtime data quality checks, and integrate it with distributed compute offerings, such as Dask, Ray, and Spark.
Speaker: Stefan Krawczyk, Manager Data Platform @ Stitch Fix

Past events (371)

ML Tech Talk: AI-Assisted Data Science

This event has passed

Photos (300)