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Hosted By
Welcome to our in-person ML monthly meetup in San Francisco. Join us for deep dive tech talks on AI/ML/Data, food/drink, networking with speakers&peer developers, and win lucky draw prizes.
[RSVP Instructions]
- Pre-register at the event website. (Correct name is required for badges and check in. Building security will require a government photo ID that matches your pre-registration at the event website. )
- Contact us to submit topics and/or sponsor the meetup on venue/food/swags/prizes. https://forms.gle/JkMt91CZRtoJBSFUA
- Join the community on Slack for events chat, speakers office hour and sharing learning, job openings, projects collaboration. join slack
Agenda:
- 5:00pm~5:30pm: Checkin, Food and Networking
- 5:30pm~5:40pm: Welcome/Sponsor intro
- 5:40pm~7:30pm: Tech talks
- 7:30pm: Open discussion, Lucky draw & Mixer
Tech Talk 1: Declarative Reasoning with Timelines: The Next Step in Event Processing
Speaker: Ben Chambers, ML CTO @Datastax/Kaskada
Abstract: At the heart of modern data processing lies events. Events describe the roughest, most complete picture available of what has happened in the world, and practically every form of data processing ultimately begins with events. While the power of event processing has increased since the emergence of streaming data processing, current systems are still difficult to use when working on problems that deal with time and order, such as predictive AI/ML. Handling these problems requires a new kind of query language – a way to declaratively reason about events over time.
In this talk, we introduce the concept of timelines. Timelines are an intuitive abstraction for reasoning about temporal values. They support a broad range of useful operations which can be efficiently computed at scale. We will demonstrate the power and differentiation of timelines:
– How timelines allow declarative queries over events and time in a simple and intuitive manner
– Why timelines are ideal for applications such as behavioral predictions, trend analysis, and forecasting, and how existing solutions such as streaming SQL fall short.
– How to execute timeline based queries using the open-source Kaskada event-processing engine.
Tech Talk 2: LLMs with end-to-end encryption
Speaker: Daniel Huynh, Co-founder @Mithril Security
Abstract: Large Language Models have become the new hot topic as models such as ChatGPT have proven their efficiency to answer a wide range of questions, from code analysis to medical answering, through email summarization. However, sending sensitive data to AI vendors creates privacy risks as control of data usage becomes complicated.
We will explore in this talk how confidential computing can be leveraged to enable users to benefit from AI models prediction, without ever having to disclose their data in clear. We will show how BlindBox (https://github.com/mithril-security/blindbox/), an open-source confidential AI solution can be used to deploy with ChatGPT-like models with privacy, such as Dolly 2.0 from Databricks or OpenChatKit from Together.
Tech Talk 3: TBD
Tuesday, May 23, 2023
5:00 PM to 8:00 PM PDT
555 California St #200 · San Francisco, CA
