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Machine Learning on the Edge with Sangeetha KP

humblefool_2 profile image SangeethaKP ・2 min read

Sangeetha is a software developer at Amazon focused on building features for the mobile shopping app. Previously she worked on building features on Echo devices and the Alexa companion app. When she is not programming, she can be found tinkering with photography, scouting around Seattle meetups or lurking on Twitter.

The world is filled with billions of small, connected, intelligent and compute-efficient smart-phones. What if we can tap into this power and do more on the edge? It turns out, ML fits perfectly here. Let us explore the MLKit library that makes it easy to do on-device Machine Learning which has several benefits such as latency and bandwidth wins, offline-first experiences, better security to name a few.

This talk covers:

  1. What is Machine Learning on the edge and what is the need for that? This section also talks about the pros and cons of doing ML on the edge.
  2. Introduction to MLKit library that helps achieve ML on the edge and what are the features it offers? This section will talk about the different use-cases one could use these features as well.
  3. Demo of an Android application built using MLKit library that demonstrates the above said features so that the audience get an understanding of MLKit in action.
  4. Code walk-through of above demo to understand ML to convey how can one get started right away with integrating MLKit in their mobile applications.
  5. Summary of talk

Here is a download link to the talk slides (PDF)

Refer this source, for more resources on this talk

This talk will be presented as part of CodeLand:Distributed on July 23. After the talk is streamed as part of the conference, it will be added to this post as a recorded video.

Discussion (10)

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ben profile image
Ben Halpern

I've been looking forward to this one

vaidehijoshi profile image
Vaidehi Joshi

Great talk, Sangeetha! I've often heard that the hardest part of machine learning is gathering and sanitizing the input data. Do you agree with that statement? And if you do, do you have any tips or recommendations for how to make that any easier?

humblefool_2 profile image
SangeethaKP Author

Definitely Vaidehi. For ML solutions in general, I love this book when I approached it as a beginner and it has a good section on cleansing data as well: Apart from that there is a write-up from Google on preparing data:

If one is lucky, one of the readily available TensorFlowLite model can be picked from here as well:

maestromac profile image
Mac Siri • Edited

You really help clarified what edge-computing is. Thank you!!

lindakatcodes profile image
Linda Thompson

Thanks for sharing this - I had no idea there was a ML option for mobile devices! Will definitely have to look into that!

humblefool_2 profile image
SangeethaKP Author

Yess! :) Definitely hit me up if you have any questions!

achavez8 profile image
Adriana Chavez

I've been attending Code for Cause's free ML bootcamp; sharing in case someone else can find it useful!

humblefool_2 profile image
SangeethaKP Author

Thanks for this link Adriana, this is useful!

nickytonline profile image
Nick Taylor (he/him)

Great talk @humblefool_2 . 👏🏻 Since Firebase is no longer a hard requirement, can any data store can be used?

humblefool_2 profile image
SangeethaKP Author

Thank you Nick! Yes Firebase is no longer a requirement for using on-device apis. You can use any data store for your application, yes! :)