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 stars77.23%
- 4 stars16.63%
- 3 stars4.55%
- 2 stars0.87%
- 1 star0.70%
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
Just one recommendation, may be an exercise on a NLP Model deployment (Text or audio) could have been added rather than all 3 examples of computer vision
Good introduction into getting TensorFlow models up and running on different platforms from microcontrollers, raspberry PI through to IOS and Android
I am glad I did this course to learn about exciting options to run Tensorflow on a variety of devices. I am thinking about Raspberry Pi and iOS devices in particular
About the TensorFlow: Data and Deployment Specialization

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