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24-May-22 tinyML Talks by Vijay Janapa Reddi from Harvard University

Announcing tinyML Talks on May 24th, 2022

IMPORTANT: Please register here

https://us02web.zoom.us/webinar/register/4716521138045/WN_Ipaqfjz7QvCo6dQ5IzS07Q

Once registered, you will receive a link and dial in information to teleconference by email, that you can also add to your calendar.

8:00 AM - 9:00 AM Pacific Daylight Time (PDT) Vijay Janapa Reddi, Associate Professor, Harvard University "MLOps for TinyML: Challenges & Directions in Operationalizing TinyML at Scale"

Over eighty percent or more of companies that attempt to integrate machine learning into operational applications fail. How could this be? Many organizations underestimate the difficulty of implementing ML. This talk emphasizes the significance of machine learning operations (MLOps) in scaling TinyML to enterprise-scale deployments that provide real-world value. Training and deploying a machine learning model on a single tiny embedded device is one thing; it is quite another to scale to thousands of devices. TinyML adds a number of embedded ecosystem-specific impediments to the conventional machine learning deployment pipeline, hence considerably complicating ML deployment even further. To address these myriad issues, the talk introduces a seven-stage MLOps architecture for operationalizing TinyML successfully. These stages range from ML model development for a fleet of heterogeneous devices to continuous monitoring for detecting data drift and everything in-between. The framework is a comprehensive end-to-end workflow for scaling TinyML deployments from a proof of concept to a real-world solution.

Vijay Janapa Reddi is an Associate Professor at Harvard University, Inference Co-chair for MLPerf, and a founding member of MLCommons, a nonprofit ML organization that aims to accelerate ML innovation. He also serves on the MLCommons board of directors.

We encourage you to register earlier since on-line broadcast capacity may be limited. Note: tinyML Talks slides and videos will be available on the tinyML website and tinyML YouTube Channel afterwards, for those who missed the live session. Please take a moment and subscribe to the YouTube channel today:


https://www.youtube.com/tinyML?sub_confirmation=1

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