Usually we imagine Machine Learning models running on expensive servers with lots of memory and resources
A change that is enabling complete new types of apps is executing ML models on the edge, like on phones and microcontrollers
Let's find out why that matters
[2min]
1/6🧵
A change that is enabling complete new types of apps is executing ML models on the edge, like on phones and microcontrollers
Let's find out why that matters
[2min]
1/6🧵
Why would you want to run a ML model on a phone?
Lower Latency: if you want to get inference on real time, running a model over a cloud API is not going to give a good user experience.
Running locally is much better and potentially much faster ⚡️
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Lower Latency: if you want to get inference on real time, running a model over a cloud API is not going to give a good user experience.
Running locally is much better and potentially much faster ⚡️
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When running ML models on-device, your app will be able to keep working even without network connectivity
For example, if your app translates text, it will still work in another country, when you need most and it won't use your hard earned money on roaming fees!
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For example, if your app translates text, it will still work in another country, when you need most and it won't use your hard earned money on roaming fees!
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ML models running on-device also preserve your privacy 🕵️ as your data won't leave the device and be used on a server somewhere of a service you might not trust yet.
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TensorFlow Lite is the framework that enables you to run TF models completely on-device.
TFLite is available to be used in multiple platforms like: Android, iOS and Microcontrollers
tensorflow.org/lite/guide/get…
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TFLite is available to be used in multiple platforms like: Android, iOS and Microcontrollers
tensorflow.org/lite/guide/get…
5/6🧵
I'll keep posting about on-device ML this week.
Leave your questions in the comments so I can address them on future posts.
6/6🧵
Leave your questions in the comments so I can address them on future posts.
6/6🧵
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