Enroll Course: https://www.coursera.org/learn/device-based-models-tensorflow
Bringing machine learning models out of the lab and into the real world is a crucial, often overlooked, step in the ML lifecycle. Coursera’s “Device-based Models with TensorFlow Lite” course tackles this challenge head-on, providing a comprehensive guide to deploying your AI creations on mobile and embedded devices. This course is the second in a specialization, building upon foundational ML knowledge to focus specifically on the practicalities of on-device inference.
The course begins with a deep dive into TensorFlow Lite (TFLite), the lightweight solution designed for mobile and resource-constrained environments. You’ll learn how TFLite optimizes models for lower power consumption and efficient processing, essential considerations for battery-operated devices. The curriculum then seamlessly transitions into practical application development, guiding you through the process of integrating TFLite models into both Android and iOS applications. Even if you’re not an expert Android or iOS developer, the course offers clear explanations and sample apps for common use cases like image classification and object detection, making it accessible to a broad audience.
A significant portion of the course is dedicated to the hands-on implementation. You’ll learn how to use the TFLite Interpreter, the core component for running models on these platforms. The course doesn’t shy away from the intricacies of model conversion from standard TensorFlow to the TFLite format, ensuring you understand the entire pipeline. For those interested in the burgeoning field of edge AI, the final module explores deployment on embedded systems, specifically mentioning Raspberry Pi and SparkFun Edge boards. While hardware access isn’t strictly necessary, the course provides guidance on running models in emulated environments, ensuring everyone can benefit from the learning experience.
Overall, “Device-based Models with TensorFlow Lite” is an invaluable resource for anyone looking to bridge the gap between model development and real-world application. It equips learners with the practical skills and knowledge needed to deploy powerful machine learning experiences directly onto users’ devices, opening up a world of possibilities for innovative mobile and edge AI solutions. Highly recommended for ML engineers, mobile developers, and anyone passionate about making AI accessible and impactful.
Enroll Course: https://www.coursera.org/learn/device-based-models-tensorflow