Engineering AI Excellence

· Azhar ul Haque Sario
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87
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Engineering AI Excellence: A Practical Guide to Building and Deploying Next-Generation AI Systems

Are you ready to push the boundaries of what's possible with AI?


The field of AI is evolving at an electrifying pace. It's no longer enough to simply build AI that works—today's AI engineers need to create systems that are robust, scalable, efficient, and ethical. "Engineering AI Excellence" equips you with the practical knowledge and cutting-edge techniques needed to design, deploy, and manage AI systems that truly stand out.


This book is your comprehensive guide to navigating the complexities of real-world AI development. Inside, you'll discover how to:


Maximize your AI investments: Learn to optimize GPU usage, ensuring you get the most performance out of your hardware without breaking the bank. (Chapter 1)

Scale AI workloads with ease: Master Kubernetes to orchestrate complex AI systems, enabling seamless scaling and efficient resource management. (Chapter 2)

Protect sensitive data: Implement federated learning techniques to train models on decentralized data while preserving privacy and security. (Chapter 3)

Deploy cost-effectively: Leverage serverless GPUs for AI inference, achieving scalability and reducing operational expenses. (Chapter 4)

Boost performance with model compression: Explore techniques like quantization and pruning to streamline your models for faster inference and reduced resource consumption. (Chapter 5)

Automate your AI pipeline: Embrace infrastructure-as-code principles and tools like Terraform to streamline deployment and management. (Chapter 6)

Ensure system reliability: Gain insights into AI observability, using monitoring and debugging tools to proactively identify and resolve issues. (Chapter 7)

Build fair and ethical AI: Understand the sources of bias in AI models and implement strategies to mitigate them, ensuring fairness and inclusivity. (Chapter 8)

Optimize model performance through experimentation: Utilize A/B testing to rigorously evaluate different model versions and identify the best performers. (Chapter 9)

Enhance system resilience: Embrace chaos engineering principles to stress-test your AI systems, uncover vulnerabilities, and build robust solutions. (Chapter 10)

"Engineering AI Excellence" is more than just a book—it's your roadmap to mastering the art of AI engineering. Whether you're a seasoned AI professional or just starting your journey, this book provides the practical guidance and in-depth knowledge you need to build AI systems that are not just functional, but truly exceptional.


Join the movement towards building a future where AI is fast, efficient, private, fair, and truly transformative.


Keywords: AI engineering, GPU optimization, Kubernetes, federated learning, serverless GPUs, model compression, AI infrastructure as code, AI observability, bias mitigation, A/B testing, chaos engineering

關於作者

Azhar ul Haque Sario is bestselling author. Data scientist. Cambridge Alumnus. I have proven technical skills (MBA, ACCA (Knowledge Level- FTMS college Malaysia), BBA, several Google certifications such as Google Data Analytics Specialization, Google Digital Marketing & E-commerce Specialization, and Google Project Management Specialization) to deliver insightful books with ten years of business experience. I have written and published 650+ titles. 


ORCID: https://orcid.org/0009-0004-8629-830X

[email protected]


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