Enterprise AI in the Cloud: A Practical Guide to Deploying End-to-End Machine Learning and ChatGPT Solutions
- 8h 34m
- Rabi Jay
- John Wiley & Sons (US)
- 2023
Embrace emerging AI trends and integrate your operations with cutting-edge solutions
Enterprise AI in the Cloud: A Practical Guide to Deploying End-to-End Machine Learning and ChatGPT Solutions is an indispensable resource for professionals and companies who want to bring new AI technologies like generative AI, ChatGPT, and machine learning (ML) into their suite of cloud-based solutions. If you want to set up AI platforms in the cloud quickly and confidently and drive your business forward with the power of AI, this book is the ultimate go-to guide. The author shows you how to start an enterprise-wide AI transformation effort, taking you all the way through to implementation, with clearly defined processes, numerous examples, and hands-on exercises. You’ll also discover best practices on optimizing cloud infrastructure for scalability and automation.
Enterprise AI in the Cloud helps you gain a solid understanding of:
- AI-First Strategy: Adopt a comprehensive approach to implementing corporate AI systems in the cloud and at scale, using an AI-First strategy to drive innovation
- State-of-the-Art Use Cases: Learn from emerging AI/ML use cases, such as ChatGPT, VR/AR, blockchain, metaverse, hyper-automation, generative AI, transformer models, Keras, TensorFlow in the cloud, and quantum machine learning
- Platform Scalability and MLOps (ML Operations): Select the ideal cloud platform and adopt best practices on optimizing cloud infrastructure for scalability and automation
- AWS, Azure, Google ML: Understand the machine learning lifecycle, from framing problems to deploying models and beyond, leveraging the full power of Azure, AWS, and Google Cloud platforms
- AI-Driven Innovation Excellence: Get practical advice on identifying potential use cases, developing a winning AI strategy and portfolio, and driving an innovation culture
- Ethical and Trustworthy AI Mastery: Implement Responsible AI by avoiding common risks while maintaining transparency and ethics
- Scaling AI Enterprise-Wide: Scale your AI implementation using Strategic Change Management, AI Maturity Models, AI Center of Excellence, and AI Operating Model
Whether you're a beginner or an experienced AI or MLOps engineer, business or technology leader, or an AI student or enthusiast, this comprehensive resource empowers you to confidently build and use AI models in production, bridging the gap between proof-of-concept projects and real-world AI deployments.
With over 300 review questions, 50 hands-on exercises, templates, and hundreds of best practice tips to guide you through every step of the way, this book is a must-read for anyone seeking to accelerate AI transformation across their enterprise.
About the Author
Rabi Jay is a recognized IT expert with over 15 years of experience working in roles such as VP of Architecture, Digital Platform Strategy Lead, and Global Alliance Manager at Deloitte Consulting, as well as at HCL America and SapientRazorfish. He has been instrumental in driving large-scale, enterprise-level Cloud and AI transformations across diverse industries like retail, telecom, finance, and tech. He holds sought-after certifications in AWS Machine Learning, AWS Solutions Architect, and Microsoft Azure. You can connect with Rabi through his authoritative LinkedIn newsletter, Enterprise AI Transformation.
In this Book
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Introduction
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Enterprise Transformation with AI in the Cloud
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Case Studies of Enterprise AI in the Cloud
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Addressing the Challenges with Enterprise AI
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Designing AI Systems Responsibly
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Envisioning and Aligning Your AI Strategy
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Developing an AI Strategy and Portfolio
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Managing Strategic Change
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Identifying Use Cases for Your AI/ML Project
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Evaluating AI/ML Platforms and Services
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Launching Your Pilot Project
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Empowering Your People Through Org Change Management
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Building Your Team
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Setting Up an Enterprise AI Cloud Platform Infrastructure
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Operating Your AI Platform with MLOps Best Practices
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Process Data and Engineer Features in the Cloud
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Choosing Your AI/ML Algorithms
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Training, Tuning, and Evaluating Models
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Deploying Your Models into Production
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Monitoring Models
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Governing Models for Bias and Ethics
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Using the AI Maturity Framework to Transform Your Business
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Setting Up Your AI COE
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Building Your AI Operating Model and Transformation Plan
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Implementing Generative AI Use Cases with ChatGPT for the Enterprise
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Planning for the Future of AI
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Continuing Your AI Journey
