Device-based Models with TensorFlow Lite

DeepLearning.AI is a specialist online education platform founded in 2017 that focuses on practical, industry-relevant training in artificial intelligence and machine learning. Designed for learners at multiple levels, its courses emphasize hands-on projects, clear conceptual foundations and tools commonly used in industry. The platform’s flexible online format makes it accessible to professionals and students worldwide who want to build skills without relocating.

Courses and specializations are structured to help learners develop applied portfolios, with real-world assignments, code notebooks and community review. Collaboration with leading practitioners ensures content remains current with industry practice, and certificate programs help demonstrate competencies to employers. The platform also supports career transitions, offering guidance on interviewing for technical roles and connecting learners with opportunities to showcase their work.

For international learners seeking concentrated, practice-oriented AI education, DeepLearning.AI provides a clear route to upskill quickly and build demonstrable expertise. Its global learner community, modular course design and emphasis on project-based learning make it a pragmatic choice for those aiming to enter research, product or engineering roles in the AI ecosystem.

This program is a non-degree course called Device-based Models with TensorFlow Lite, taught in English at DeepLearning.AI in California. It helps students learn how to run machine learning models on mobile applications and embedded systems. The course lasts several weeks.

The curriculum includes four modules that cover preparing models for lower-powered devices, executing models on Android and iOS platforms, and deploying on embedded systems using TensorFlow on Raspberry Pi and microcontrollers. Students will learn how to use data more effectively to train their models and navigate various deployment scenarios.

After completing this program, students can pursue careers as Machine Learning Engineers, Data Scientists, Mobile App Developers, or Embedded Systems Engineers. They can work in industries such as technology, healthcare, or finance, and can be employed by companies that develop mobile applications or embedded systems.