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Machine Learning Engineering in Action
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Machine Learning System Design
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Machine Learning System Design

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AI at the Edge Solving Real-World Problems with Embedded Machine Learning (Daniel Situnayake, Jenny Plunkett)
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AI at the Edge Solving Real-World Problems with Embedded Machine Learning (Daniel Situnayake, Jenny Plunkett)

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AI Value Creators Beyond the Generative AI User Mindset (Rob Thomas, Paul Zikopoulos, Kate Soule etc.)
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Architecting Data and Machine Learning Platforms Enable Analytics and AI-Driven Innovation in the Cloud (Marco Tranquillin, Valliappa Lakshmanan etc.)
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Architecting Data and Machine Learning Platforms Enable Analytics and AI-Driven Innovation in the Cloud (Marco Tranquillin, Valliappa Lakshmanan etc.)

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Azure AI Services at Scale for Cloud, Mobile, and Edge Building Intelligent Apps with Azure Cognitive Services and Machine... (Simon Bisson, Mary Branscombe, Chris Hoder etc.)
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Azure AI Services at Scale for Cloud, Mobile, and Edge Building Intelligent Apps with Azure Cognitive Services and Machine... (Simon Bisson, Mary Branscombe, Chris Hoder etc.)

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Building Recommendation Systems in Python and JAX Hands-On Production Systems at Scale (Bryan Bischof, Hector Yee)

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Effective Machine Learning Teams Best Practices for Ml Practitioners (David Tan, Ada Leung, David Colls)
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Implementing MLOps in the Enterprise A Production-First Approach (Yaron Haviv, Noah Gift)
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Implementing MLOps in the Enterprise A Production-First Approach (Yaron Haviv, Noah Gift)

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Infrastructure as Code Dynamic Systems for the Cloud Age (Kief Morris)
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Infrastructure as Code Dynamic Systems for the Cloud Age (Kief Morris)

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Practical Deep Learning for Cloud, Mobile, and Edge Real-World AI Computer-Vision Projects Using Python, Keras TensorFlow (Anirudh Koul, Siddha Ganju, Meher Kasam)

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Python for Algorithmic Trading From Idea to Cloud Deployment (Yves Hilpisch)

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Scaling Machine Learning with Spark Distributed ML with MLlib, TensorFlow, and PyTorch (Adi Polak)
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TinyML Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers (Pete Warden, Daniel Situnayake)
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AI and Machine Learning for On-Device Development A Programmers Guide (Moroney, Laurence)
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Building Machine Learning Powered Applications Going from Idea to Product (Emmanuel Ameisen)
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Data Science on AWS Implementing End-to-End, Continuous AI and Machine Learning Pipelines (Chris Fregly, Antje Barth)
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Kubeflow for Machine Learning From Lab to Production (Trevor Grant, Holden Karau, Boris Lublinsky etc.)
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Kubeflow for Machine Learning From Lab to Production (Trevor Grant, Holden Karau, Boris Lublinsky etc.)

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Machine Learning Design Patterns Solutions to Common Challenges in Data Preparation, Model Building, and MLOps (Valliappa Lakshmanan, Sara Robinson etc.)
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