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30 Agents Every AI Engineer Must Build. Transform LLMs into autonomous decision-making vertical agents in healthcare, finance, and beyond Imran Ahmad

Język publikacji: angielski
30 Agents Every AI Engineer Must Build. Transform LLMs into autonomous decision-making vertical agents in healthcare, finance, and beyond Imran Ahmad - okladka książki

30 Agents Every AI Engineer Must Build. Transform LLMs into autonomous decision-making vertical agents in healthcare, finance, and beyond Imran Ahmad - okladka książki

Autor:
Imran Ahmad
Serie wydawnicze:
Hands-on
Ocena:
As AI evolves from passive tools into proactive collaborators, intelligent agents lead this transformative shift. This guide equips you with critical knowledge on agent architectures, practical tools, and industry insights to develop robust, autonomous AI systems. You'll start by mastering foundational agent capabilities such as perception, memory, reasoning, planning, and learning. Gain insight into the cognitive loops essential for autonomous systems and build agent architectures using state-of-the-art frameworks like LangChain and LangGraph. Practical industry applications are explored across healthcare, finance, manufacturing, and education—illustrating how agents can optimize workflows, enhance advisory systems, automate quality control, and enable adaptive learning environments. Through numerous real-world examples, this book guides you in creating intelligent agents capable of contextual reasoning, effective tool utilization, real-time responsiveness, and seamless collaboration with humans. Additionally, you'll learn crucial strategies for the deployment, management, and ethical development of responsible AI systems. Whether you're developing your first intelligent agent or enhancing critical business operations, this book provides clear, actionable guidance for creating scalable and ethically robust AI solutions.

O autorze książki

Imran Ahmad jest certyfikowanym instruktorem Google z wieloletnim doświadczeniem. Wykłada Pythona, uczenie maszynowe i głębokie, algorytmikę oraz zagadnienia big data. Przez ostatnie lata pracował w rządowym laboratorium Kanady nad projektem z zakresu uczenia maszynowego. Obecnie zajmuje się algorytmami używającymi GPU do optymalnego trenowania złożonych modeli uczenia maszynowego.

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