ML and Data Processes at Scale


Apr 19, 05:00 PM EDT
  • Atlanta (US) (Improving, 11675 Rainwater Dr #100, Alpharetta, GA ...) AICamp
  • 45 RSVP
Description
Speaker

Welcome to our in-person monthly ML meetup in Atlanta. Join us for deep dive tech talks on AI/ML, food/drink, networking with speakers&peers developers

Agenda (EST):
* 5:00pm~5:30pm: Checkin, Food/drink and networking
* 5:30pm~5:40pm: Welcome/community update/Sponsor intro
* 5:40pm~7:00pm: Tech talks
* 7:00pm: Open discussion & Mixer

Tech Talk 1: The Fundamentals of Type-safe, Reproducible, and Scalable Data Pipelines
Speaker: Niels Bantilan @Union.ai
Abstract: Data science (DS) and machine learning (ML) pose additional challenges to the traditional software development lifecycle. How do I deploy the models or analyses developed in the lab to production in a way that’s robust, reproducible, and scalable?
Flyte provides an orchestration, scheduling, and execution platform for DS and ML practitioners. Flytekit, the Python SDK for Flyte, enables them to write business logic as isolated tasks, compose them into more complex workflows, and horizontally/vertically scale compute resources.
Attendees will learn the fundamentals of how to create DS/ML pipelines in Flytekit by composing tasks together into workflows, see how Flytekit uses Python’s typing module to provide type safety and out-of-the-box data lineage tracking, and gain an understanding about how Flyte leverages Docker to provide strong reproducibility guarantees. Finally, they will learn about how DS and ML practitioners can seamlessly optimize the performance of their pipelines by configuring the resource and caching requirements of their Flytekit tasks in pure Python.

Venue:
Improving
11675 Rainwater Dr #100, Alpharetta, GA 30009 Google Map

Lucky draw
We will raffle winners for prizes during the event. To enter the lucky draw, share the event on social media:

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    Niels Bantilan

    Niels Bantilan is the chief machine learning engineer at Union.ai. He maintains Flyte and is the creator of UnionML and Pandera. His research interests include reinforcement learning, AutoML, creative machine learning, and fairness, accountability, and transparency in automated systems. He enjoys developing open source tools to make data science and machine learning practitioners more productive.
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