Machine Learning Systems with TinyML
"Machine Learning Systems with TinyML" simplifies AI system development by covering ML pipelines, data collection, model design, optimization, security, and integration. It emphasizes TinyML for accessibility, addressing model architectures, training, inference, and critical considerations. The open-source book encourages collaboration and innovation in AI technology.
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"Machine Learning Systems with TinyML" is a comprehensive guide that delves into the world of AI systems, focusing on applied machine learning concepts. The book aims to simplify the development of robust ML pipelines essential for deployment, covering key phases like data collection, model design, optimization, acceleration, security, and integration from a systems perspective. It uses TinyML as a tool for accessibility and covers designing ML model architectures, hardware-aware training strategies, inference optimization, and benchmarking methodologies. The text also explores critical considerations such as reliability, privacy, responsible AI, and solution validation. The open-source nature of the book encourages collaboration and continuous updates to keep pace with the evolving AI landscape. Readers are invited to contribute to this living document, fostering a community-driven approach to knowledge sharing and innovation in AI technology. The book is designed for individuals with a basic understanding of computer science concepts and a curiosity to explore AI systems, offering a blend of expert knowledge and practical insights for navigating the complexities of AI engineering.
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