tinyML Talks on May 24, 2022 “MLOps for TinyML: Challenges & Directions in Operationalizing TinyML at Scale” by Vijay Janapa Reddi

We held our next tinyML Talks webcast. Vijay Janapa Reddi from Harvard University presented MLOps for TinyML: Challenges & Directions in Operationalizing TinyML at Scale on May 24, 2022.

March 16 forum

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.


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Vijay Janapa Reddi

Download presentation slides:
Vijay Janapa Reddi

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