Edge Ai With Synapse Ai
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Edge AI with Synapse AI
Author: Darryl Jeffery
language: en
Publisher: Independently Published
Release Date: 2025-04-28
Edge AI with Synapse AI: The Practical Guide to Deploying Optimized On-Device Models from Azure Synapse for Real-Time Inference and Privacy is your definitive roadmap to bringing high-performance AI to the edge. This book shows you how to design, train, and optimize models in Azure Synapse, convert them into lightweight ONNX and TensorFlow Lite formats, and deploy them securely to devices-from Raspberry Pi gateways to ARM-based microcontrollers. You'll learn to integrate real-time LLM calls and retrieval-augmented generation into multi-agent workflows, leverage MCP servers for seamless agent orchestration, and automate intelligent pipelines with n8n, all while preserving user privacy and minimizing bandwidth. Drawing on hands-on examples, you'll master advanced techniques-quantization, pruning, operator fusion, and federated learning-that shrink model footprints and accelerate inference without sacrificing accuracy. You'll explore how to build agentic AI systems that coordinate multiple specialized models, implement canary releases and rollbacks for safe updates, and visualize fleet-wide telemetry in the Synapse dashboard. Practical code illustrations guide you through Python-based data pipelines, real-time REST APIs, and secure over-the-air update mechanisms. Whether you're a machine-learning engineer, DevOps professional, or AI architect, this book equips you with the skills to build autonomous AI agents, integrate RAG 2.0 pipelines, and harness emerging accelerators such as Coral Edge TPU and Habana Gaudi. From manufacturing anomaly detection to smart-city sensor networks and remote patient monitoring, you'll see how edge-first intelligence transforms applications across industries. Embrace the edge-to-cloud continuum and deliver resilient, low-latency AI solutions that safeguard privacy and scale with confidence.
Artificial Intelligence and Machine Learning for EDGE Computing
Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms. Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering. - Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing - Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers - Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints
Revolutionizing AI with Brain-Inspired Technology: Neuromorphic Computing
As artificial intelligence (AI) continues to evolve, neuromorphic computing stands at the forefront of this revolution, offering a transformative approach by mimicking the structure and function of the human brain. This cutting-edge technology is reshaping AI, making it more efficient, adaptive, and capable of complex tasks that were once thought impossible. Neuromorphic computing has the potential to revolutionize industries such as healthcare, robotics, and autonomous vehicles, driving advancements that could redefine the future of technology and its applications in everyday life. Understanding this emerging field is crucial for anyone involved in AI development or interested in the next frontier of technological innovation. Revolutionizing AI with Brain-Inspired Technology: Neuromorphic Computing covers neuromorphic computing, its real-world applications, and the latest advancements pushing the boundaries of AI. By offering a comprehensive overview and inspiring new research, this book plays a pivotal role in shaping the future of AI and its impact on various sectors. This volume is an essential resource for researchers, academics, professionals, and policymakers who seek to understand the principles and potential of neuromorphic computing as well as the societal implications of these technologies.