Machine learning models + IoT data = a smarter world (Google I/O '18)

With the IoT market set to triple in size by 2020, and massive increases in computing power on small devices, the intersection of IoT and machine learning is a trend that all developers should pay attention to. This talk will cover three core use cases, including: how to manage sourcing data from IoT devices to drive machine-learned models; how to deploy and use trained models on mobile devices; and how to do on-device training with a Raspberry Pi computer.
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#io18 event: Google I/O 2018; re_ty: Publish; product: Cloud - Internet of Things (IoT) - IoT Core, Cloud - Data Analytics - PubSub, Cloud - Containers - Google Kubernetes Engine (GKE), TensorFlow - General, Cloud - AI and Machine Learning - AI Platform; fullname: Laurence Moroney, Kaz Sato; event: Google I/O 2018;

[@somnath2025]

Hi, I come from an Electronics and Telecommunication background with 10 years of Telecom Domain experience , and at present trying to find my way to full time Machine Learning Engineer + IOT Engineer. To me i always look from a end product perspective , something can be commercialized so that people can benefit from it. Your talk gave me real time use of how DS+MS+ML+DL+IOT can be used in real time . I am looking for more use cases , something I can try on my own. So thank you again for your time making this video, it was really insightful to the real world of AI in the future.

[@gigajoules6636]

Any resources you can recommend?

[@humaaj8895]

hello can i have your email i need your help in my research area

[@motog9464]

By counting of the number of views and the number of comments, I can. Say that only few people are actually love to develop the things or in simple words the creators while all others are just the customers.
Hope future is bright 👍
Awesome presentation sir.

[@PrasannaVarshan]

If I were you I would count the top talents as the people who went in-person for the i/o

[@redregar2522]

Only a few understand how the future will work and also want the changes.

[@grahamcox5784]

Very interesting ML and IoT talk. Really excited to try out the links referenced. Speakers and demos were really inspirational 👏🏼

[@laurencemoroney655]

Thanks Graham! :)

[@chirag6108]

Thank you so much to both of you and the team.
Explained in a very simple way and was easily able to connect. Looking forward to build the demo projects soon.

[@nguyenthanhdat93]

Thanks for sharing! Neat combination between ML and IoT

[@LaurenceMoroney]

Thanks Dat! :)

[@MaiDaly]

Enjoyable and informative talk. Thanks for this.

[@LaurenceMoroney]

THanks for the feedback! :)

[@Bozojimmy]

is arduino + the cheap AQ sensor reliable enough for a commercial implementation ?

[@khalidben9940]

nice video I am interested in this field

[@bjugdbjk]

Excellent, really cool,I got inspired, don't know y these videos deserve r not getting lot f views... great talk

[@zaccanter6465]

Grateful for this video. But the first sensor shown is definitely not an air quality sensor. Just a simple CO2 sensor. Imo a solid small and reliable air quality sensor does not yet exist. Honestly that first demo is kinda a sham.

[@theailateshow368]

Exciting times

[@72ohit]

thanks (y)

[@brefat]

That's Awesome

[@alvaromartin6301]

I love it

[@richajain5657]

This is a great video. A friend and I worked on a similar idea in grad school (th-cam.com/video/YuS6B1hL_h8/w-d-xo.html) and so inspiring to see that its real world application are no far away.

[@ReelsRail]

shopping cat🐱