Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.
This course is part of the TensorFlow: Data and Deployment Specialization
Offered By


About this Course
Basic understanding of Kotlin and/or Swift
What you will learn
Prepare models for battery-operated devices
Execute models on Android and iOS platforms
Deploy models on embedded systems like Raspberry Pi and microcontrollers
Skills you will gain
- TensorFlow Lite
- Mathematical Optimization
- Machine Learning
- Tensorflow
- Object Detection
Basic understanding of Kotlin and/or Swift
Offered by
Syllabus - What you will learn from this course
Device-based models with TensorFlow Lite
Running a TF model in an Android App
Building the TensorFLow model on IOS
TensorFlow Lite on devices
Reviews
- 5 stars77.09%
- 4 stars16.60%
- 3 stars4.72%
- 2 stars0.87%
- 1 star0.69%
TOP REVIEWS FROM DEVICE-BASED MODELS WITH TENSORFLOW LITE
Great course, very practical in the real world. It also balances and accommodates developers on what devices you have available. Looking forward to the next course
Great course for ML on Microcontroller and Mobile Devices, got a strong foundation to learn more in this field of Edge ML with TensorFlow Lite.
It's a bit fast and definitely tightly packed. The objectives are clear though --how to build/debug/deploy on various modern devices (Android, iOS, RaspPi, etc)
Laurence is a good teacher. His explanations are clear and to the point. This is a one of the best sources to learn how to deploy ML models on devices
About the TensorFlow: Data and Deployment Specialization

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