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.
Rate this session by signing-in on the I/O website here → goo.gl/rYcGev
Watch more IoT sessions from I/O '18 here → goo.gl/xfowJ8
See all the sessions from Google I/O '18 here → goo.gl/q1Tr8x
Subscribe to the Google Developers channel → goo.gl/mQyv5L
#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;
Негізгі бет Machine learning models + IoT data = a smarter world (Google I/O '18)
No video
Пікірлер: 23