Making intelligent Android apps
summary
Covers using on-device machine learning to enhance Android app user experience, focusing on integrating ML models directly into apps (rather than cloud-only solutions). Mentions technologies and practices relevant to Android developers for deploying and running ML on-device.
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chapters (27)
- 0:00Start
- 2:34Agenda
- 2:59What is Machine learning
- 4:21More often that you think
- 5:22How is it different from Al
- 6:59Why is it such a big deal today
- 7:54New form factors
- 10:30Why on device?
- 12:27But it's hard
- 13:36ML KIT
- 14:07TensorFlow Lite
- 14:40TF-lite architecture
- 23:06TF lite size
- 23:58Common concerns
- 24:36Some of tflite users
- 25:19What devs liked
- 26:26What more they wanted
- 28:06How to use Tf Lite
- 28:55Pretrained & Retrained Models
- 30:25Custom models
- 30:39Build and train a model
- 30:53Converting the model
- 31:10Validating and Profiling the model
- 32:33Deploying the model
- 33:00Optimize models
- 33:36Model Optimization toolkit
- 34:45Roadmap 2019 & Beyond
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