Artificial Intelligence of Things AIoT: https://
techtarget.com/iotagenda/defi
nition/Artificial-Intelligence-of-Things-AIoT
… AIoT book v/ @PacktDataML → Hands-On AI for IoT — Expert Machine Learning & Deep Learning techniques for developing smarter IoT systems [2nd Ed.]: http://
amzn.to/3YAu6hS 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
Leverage the
@kirkdborne
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A Guide Applies Machine Learning to IoT Systems
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A Guide Builds Online Machine Learning Models for Streaming Data
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Machine Learning for Streaming Data with Python — build practical online Machine Learning solutions: http://
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A Python Guide Explains MCP for Building Agentic Systems
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Learn Model Context Protocol [MCP] with Python — Build Agentic Systems in Python with the new standard for AI Capabilities: http://
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Understand the MCP protocol and its core components -
A Guide Uses RAG Memory and Graphs for Agent Recall
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2nd Edition, by @keithbourne v/ @PacktDataML "Unlocking Data with Generative AI and RAG — Learn AI Agent Fundamentals with RAG-powered Memory, Graph-based RAG, and Intelligent Recall" Get the book here: http://
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LangChain Guide Covers Production LLM Apps and Advanced Agents
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The 2nd Edition of this book has arrived, with Agentic AI updates: "Generative AI with LangChain — Build Production-ready LLM Applications and Advanced Agents using Python and LangGraph" at http://
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Design and -
LangChain and Vertex AI Guide Builds Scalable Generative Applications
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Generative AI on Google Cloud with LangChain — Design scalable Generative AI solutions with Python, LangChain, and Vertex AI on Google Cloud: http://
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Turn challenges into opportunities by learning advanced techniques for -
A Guide Designs Context Engines for Multi-Agent Systems
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"Context Engineering for Multi-Agent Systems: Move beyond prompting to build a Context Engine, a transparent architecture of context and reasoning" — at http://
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Develop memory models to retain short-term and -
Agent Architecture Guide Covers Multi-Agent Systems, RAG and LLMOps
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574 information-packed pages from @PacktDataML at http://
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Updated AI Agents in Action Covers LLMs, Prompting and MCP
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AI Agents in Action: http://
amzn.to/3RiYRqX v/ @ManningBooks New! Updated! 2nd Edition! Table of Contents:
1. The rise of AI agents
2. Core components: Large language models, prompting, and agents
3. Actions with Model Context Protocol for AI agents
4. Architecting and -
Agentic AI Engineering Guide Explains Reasoning Loops and Deployment
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Agentic AI Engineering for Systems that Reason and Act Autonomously – Design, Build, and Prompt LLM-Based Agents for Real-World Deployment: http://
amzn.to/4eXSPnC Main Topics:
• How autonomous systems differ from automation
• Reasoning loops and core components
• Designing