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
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)
Was a great course.Had some hands on experience on codes using Gcollabs.Additionally helped me complete my project of deploying ML on android.
Perfect course to learn about TensorflowLite and deploying tflite models on various devices. Excellent instructor and course structure. This is one that I was looking for!
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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