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:
- 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.
- 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.
- 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.
- 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.
- Summary of talk
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.