Enterprise AI in the Cloud: A Practical Guide to Deploying End-To-End Machine Learning and ChatGPT Solutions
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Newark
John Wiley & Sons, Incorporated
2024
|
| Ausgabe: | 1st ed |
| Schriftenreihe: | Tech Today Series
|
| Links: | https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=31036205 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Umfang: | 1 Online-Ressource (527 Seiten) |
| ISBN: | 9781394213078 |
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| 245 | 1 | 0 | |a Enterprise AI in the Cloud |b A Practical Guide to Deploying End-To-End Machine Learning and ChatGPT Solutions |
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| 505 | 8 | |a Cover -- Title Page -- Copyright Page -- Acknowledgments -- About the Author -- About the Technical Editor -- Contents -- Introduction -- How This Book Is Organized -- Who Should Read This Book? -- Data Scientists and AI Teams -- IT Leaders and Teams -- Students and Academia -- Consultants and Advisors -- Business Strategists and Leaders -- C-Level Executives -- Why You Should Read This Book -- Unique Features -- Comprehensive Coverage of All Aspects of Enterprise-wide AI Transformation -- Case Study Approach -- Coverage of All Major Cloud Platforms -- Discussion of Nontechnical Aspects of AI -- Best Practices for MLOps and AI Governance -- Up-to-Date Content -- Hands-on Approach -- Part I Introduction -- Chapter 1 Enterprise Transformation with AI in the Cloud -- Understanding Enterprise AI Transformation -- Why Some Companies Succeed at Implementing AI and ML While Others Fail -- Transform Your Company by Integrating AI, ML and Gen AI into Your Business Processes -- Adopt AI-First to Become World-Class -- Importance of an AI-First Strategy -- Prioritize AI and Data Initiatives -- Leveraging Enterprise AI Opportunities -- Enable One-to-One, Personalized, Real-Time Service for Customers at Scale -- Enterprise-wide AI Opportunities -- Growing Industry Adoption of AI -- Workbook Template - Enterprise AI Transformation Checklist -- Summary -- Review Questions -- Answer Key -- Chapter 2 Case Studies of Enterprise AI in the Cloud -- Case Study 1: The U.S. Government and the Power of Humans and Machines Working Together to Solve Problems at Scale -- Revolutionizing Operations Management with AI/ML -- Enabling Solutions for Improved Operations -- Case Study 2: Capital One and How It Became a Leading Technology Organization in a Highly Regulated Environment -- Building Amazing Experiences Due to Data Consolidation | |
| 505 | 8 | |a Becoming Agile and Scalable by Moving Data Centers Into the Cloud -- Building a Resilient System by Embracing Cloud-Native Principles -- Impact of Cloud-First Thinking on DevOps, Agile Development, and Machine Learning -- Becoming an AI-First Company: From Cloud Adoption to Thrilling Customer Experiences -- Case Study 3: Netflix and the Path Companies Take to Become World-Class -- Cloud and AI Technology: A Game-Changer for Netflix's Business Model and Success -- Cloud Infrastructure and AI Adoption Drives Process Transformation -- Process Transformation Drives Organizational Change -- Workbook Template - AI Case Study -- Summary -- Review Questions -- Answer Key -- Part II Strategizing and Assessing for AI -- Chapter 3 Addressing the Challenges with Enterprise AI -- Challenges Faced by Companies Implementing Enterprise-wide AI -- Business-Related Challenges -- Data- and Model-Related Challenges -- Platform-Related Challenges -- How Digital Natives Tackle AI Adoption -- They Are Willing to Take Risks -- They Have an Advantage in Data Collection and Curation Capabilities -- They Attract Top Talent Through Competitive Compensation and Perks -- Get Ready: AI Transformation Is More Challenging Than Digital Transformation -- Complexities of Skill Sets, Technology, and Infrastructure Integration -- The Importance of Data Infrastructure and Governance -- Change Management to Redefine Work Processes and Employee Mindsets -- Regulatory Concerns: Addressing Bias, Ethical, Privacy, and Accountability Risks -- Choosing Between Smaller PoC Point Solutions and Large-Scale AI Initiatives -- The Challenges of Implementing a Large-Scale AI Initiative -- Navigate the Moving Parts, Stakeholders, and Technical Infrastructure -- Resource Allocation Challenges in Large-Scale AI Initiatives -- Overcome Resistance to Change | |
| 505 | 8 | |a Data Security, Privacy, Ethics, Compliance, and Reputation -- Build a Business Case for Large-Scale AI Initiatives -- Factors to Consider -- Workbook Template: AI Challenges Assessment -- Summary -- Review Questions -- Answer Key -- Chapter 4 Designing AI Systems Responsibly -- The Pillars of Responsible AI -- Robust AI -- Collaborative AI -- Trustworthy AI -- Scalable AI -- Human-centric AI -- Workbook Template: Responsible AI Design Template -- Summary -- Review Questions -- Answer Key -- Chapter 5 Envisioning and Aligning Your AI Strategy -- Step-by-Step Methodology for Enterprise-wide AI -- The Envision Phase -- The Align Phase -- Workbook Template: Vision Alignment Worksheet -- Summary -- Review Questions -- Answer Key -- Chapter 6 Developing an AI Strategy and Portfolio -- Leveraging Your Organizational Capabilities for Competitive Advantage -- Focus Areas to Build Your Competitive Advantage -- Driving Competitive Advantage Through AI -- Initiating Your Strategy and Plan to Kickstart Enterprise AI -- Manage Your AI Strategy, Portfolio, Innovation, Product Lifecycle, and Partnerships -- Define Your AI Strategy to Achieve Business Outcomes -- Prioritize Your Portfolio -- Strategy and Execution Across Phases -- Workbook Template: Business Case and AI Strategy -- Summary -- Review Questions -- Answer Key -- Chapter 7 Managing Strategic Change -- Accelerating Your AI Adoption with Strategic Change Management -- Phase 1: Develop an AI Acceleration Charter and Governance Mechanisms for Your AI Initiative -- Phase 2: Ensure Leadership Alignment -- Phase 3: Create a Change Acceleration Strategy -- Workbook Template: Strategic Change Management Plan -- Summary -- Review Questions -- Answer Key -- Part III Planning and Launching a Pilot Project -- Chapter 8 Identifying Use Cases for Your AI/ML Project -- The Use Case Identification Process Flow | |
| 505 | 8 | |a Educate Everyone as to How AI/ML Can Solve Business Problems -- Define Your Business Objectives -- Identify the Pain Points -- Start with Root-Cause Analysis -- Identify the Success Metrics -- Explore the Latest Industry Trends -- Review AI Applications in Various Industries -- Map the Use Case to the Business Problem -- Prioritizing Your Use Cases -- Define the Impact Criteria -- Define the Feasibility Criteria -- Assess the Impact -- Assess the Feasibility -- Prioritize the Use Cases -- Review and Refine the Criteria -- Choose the Right Model -- Use Cases to Choose From -- AI Use Cases for DevOps -- AI for Healthcare and Life Sciences -- AI Enabled Contact Center Use Cases -- Business Metrics Analysis -- Content Moderation -- AI for Financial Services -- Cybersecurity -- Digital Twinning -- Identity Verification -- Intelligent Document Processing -- Intelligent Search -- Machine Translation -- Media Intelligence -- ML Modernization -- ML-Powered Personalization -- Computer Vision -- Personal Protective Equipment -- Generative AI -- Workbook Template: Use Case Identification Sheet -- Summary -- Review Questions -- Answer Key -- Chapter 9 Evaluating AI/ML Platforms and Services -- Benefits and Factors to Consider When Choosing an AI/ML Service -- Benefits of Using Cloud AI/ML Services -- Factors to Consider When Choosing an AI/ML Service -- AWS AI and ML Services -- AI Services -- Amazon SageMaker -- AI Frameworks -- Differences Between Machine Learning Algorithms, Models, and Services -- Core AI Services -- Text and Document Services -- Chatbots: Amazon Lex -- Speech -- Vision Services -- Specialized AI Services -- Business Processing Services -- Kendra for Search -- Code and DevOps -- Industrial Solutions -- Healthcare Solutions -- Machine Learning Services -- Amazon SageMaker -- Amazon SageMaker Canvas -- SageMaker Studio Lab | |
| 505 | 8 | |a The Google AI/ML Services Stack -- For Data Scientists -- For Developers -- The Microsoft AI/ ML Services Stack -- Azure Applied AI Services -- Azure Cognitive Services -- Azure Machine Learning -- Other Enterprise Cloud AI Platforms -- Dataiku -- DataRobot -- KNIME -- IBM Watson -- Salesforce Einstein AI -- Oracle Cloud AI -- Workbook Template: AI/ML Platform Evaluation Sheet -- Summary -- Review Questions -- Answer Key -- Chapter 10 Launching Your Pilot Project -- Launching Your Pilot -- Planning for Launch -- Recap of the Envision Phase -- Planning for the Machine Learning Project -- Following the Machine Learning Lifecycle -- Business Goal Identification -- Machine Learning Problem Framing -- Data Processing -- Model Development -- Model Deployment -- Model Monitoring -- Workbook Template: AI/ML Pilot Launch Checklist -- Summary -- Review Questions -- Answer Key -- Part IV Building and Governing Your Team -- Chapter 11 Empowering Your People Through Org Change Management -- Succeeding Through a People-centric Approach -- Evolve Your Culture for AI Adoption, Innovation, and Change -- Redesign Your Organization for Agility and Innovation with AI -- Aligning Your Organization Around AI Adoption to Achieve Business Outcomes -- Workbook Template: Org Change Management Plan -- Summary -- Review Questions -- Answer Key -- Note -- Chapter 12 Building Your Team -- Understanding the Roles and Responsibilities in an ML Project -- Build a Cross-Functional Team for AI Transformation -- Adopt Cloud and AI to Transform Current Roles -- Customize Roles to Suit Your Business Goals and Needs -- Workbook Template: Team Building Matrix -- Summary -- Review Questions -- Answer Key -- Part V Setting Up Infrastructure and Managing Operations -- Chapter 13 Setting Up an Enterprise AI Cloud Platform Infrastructure -- Reference Architecture Patterns for Typical Use Cases | |
| 505 | 8 | |a Customer 360-Degree Architecture | |
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Datensatz im Suchindex
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| adam_text | |
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| author | Jay, Rabi |
| author_facet | Jay, Rabi |
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| contents | Cover -- Title Page -- Copyright Page -- Acknowledgments -- About the Author -- About the Technical Editor -- Contents -- Introduction -- How This Book Is Organized -- Who Should Read This Book? -- Data Scientists and AI Teams -- IT Leaders and Teams -- Students and Academia -- Consultants and Advisors -- Business Strategists and Leaders -- C-Level Executives -- Why You Should Read This Book -- Unique Features -- Comprehensive Coverage of All Aspects of Enterprise-wide AI Transformation -- Case Study Approach -- Coverage of All Major Cloud Platforms -- Discussion of Nontechnical Aspects of AI -- Best Practices for MLOps and AI Governance -- Up-to-Date Content -- Hands-on Approach -- Part I Introduction -- Chapter 1 Enterprise Transformation with AI in the Cloud -- Understanding Enterprise AI Transformation -- Why Some Companies Succeed at Implementing AI and ML While Others Fail -- Transform Your Company by Integrating AI, ML and Gen AI into Your Business Processes -- Adopt AI-First to Become World-Class -- Importance of an AI-First Strategy -- Prioritize AI and Data Initiatives -- Leveraging Enterprise AI Opportunities -- Enable One-to-One, Personalized, Real-Time Service for Customers at Scale -- Enterprise-wide AI Opportunities -- Growing Industry Adoption of AI -- Workbook Template - Enterprise AI Transformation Checklist -- Summary -- Review Questions -- Answer Key -- Chapter 2 Case Studies of Enterprise AI in the Cloud -- Case Study 1: The U.S. Government and the Power of Humans and Machines Working Together to Solve Problems at Scale -- Revolutionizing Operations Management with AI/ML -- Enabling Solutions for Improved Operations -- Case Study 2: Capital One and How It Became a Leading Technology Organization in a Highly Regulated Environment -- Building Amazing Experiences Due to Data Consolidation Becoming Agile and Scalable by Moving Data Centers Into the Cloud -- Building a Resilient System by Embracing Cloud-Native Principles -- Impact of Cloud-First Thinking on DevOps, Agile Development, and Machine Learning -- Becoming an AI-First Company: From Cloud Adoption to Thrilling Customer Experiences -- Case Study 3: Netflix and the Path Companies Take to Become World-Class -- Cloud and AI Technology: A Game-Changer for Netflix's Business Model and Success -- Cloud Infrastructure and AI Adoption Drives Process Transformation -- Process Transformation Drives Organizational Change -- Workbook Template - AI Case Study -- Summary -- Review Questions -- Answer Key -- Part II Strategizing and Assessing for AI -- Chapter 3 Addressing the Challenges with Enterprise AI -- Challenges Faced by Companies Implementing Enterprise-wide AI -- Business-Related Challenges -- Data- and Model-Related Challenges -- Platform-Related Challenges -- How Digital Natives Tackle AI Adoption -- They Are Willing to Take Risks -- They Have an Advantage in Data Collection and Curation Capabilities -- They Attract Top Talent Through Competitive Compensation and Perks -- Get Ready: AI Transformation Is More Challenging Than Digital Transformation -- Complexities of Skill Sets, Technology, and Infrastructure Integration -- The Importance of Data Infrastructure and Governance -- Change Management to Redefine Work Processes and Employee Mindsets -- Regulatory Concerns: Addressing Bias, Ethical, Privacy, and Accountability Risks -- Choosing Between Smaller PoC Point Solutions and Large-Scale AI Initiatives -- The Challenges of Implementing a Large-Scale AI Initiative -- Navigate the Moving Parts, Stakeholders, and Technical Infrastructure -- Resource Allocation Challenges in Large-Scale AI Initiatives -- Overcome Resistance to Change Data Security, Privacy, Ethics, Compliance, and Reputation -- Build a Business Case for Large-Scale AI Initiatives -- Factors to Consider -- Workbook Template: AI Challenges Assessment -- Summary -- Review Questions -- Answer Key -- Chapter 4 Designing AI Systems Responsibly -- The Pillars of Responsible AI -- Robust AI -- Collaborative AI -- Trustworthy AI -- Scalable AI -- Human-centric AI -- Workbook Template: Responsible AI Design Template -- Summary -- Review Questions -- Answer Key -- Chapter 5 Envisioning and Aligning Your AI Strategy -- Step-by-Step Methodology for Enterprise-wide AI -- The Envision Phase -- The Align Phase -- Workbook Template: Vision Alignment Worksheet -- Summary -- Review Questions -- Answer Key -- Chapter 6 Developing an AI Strategy and Portfolio -- Leveraging Your Organizational Capabilities for Competitive Advantage -- Focus Areas to Build Your Competitive Advantage -- Driving Competitive Advantage Through AI -- Initiating Your Strategy and Plan to Kickstart Enterprise AI -- Manage Your AI Strategy, Portfolio, Innovation, Product Lifecycle, and Partnerships -- Define Your AI Strategy to Achieve Business Outcomes -- Prioritize Your Portfolio -- Strategy and Execution Across Phases -- Workbook Template: Business Case and AI Strategy -- Summary -- Review Questions -- Answer Key -- Chapter 7 Managing Strategic Change -- Accelerating Your AI Adoption with Strategic Change Management -- Phase 1: Develop an AI Acceleration Charter and Governance Mechanisms for Your AI Initiative -- Phase 2: Ensure Leadership Alignment -- Phase 3: Create a Change Acceleration Strategy -- Workbook Template: Strategic Change Management Plan -- Summary -- Review Questions -- Answer Key -- Part III Planning and Launching a Pilot Project -- Chapter 8 Identifying Use Cases for Your AI/ML Project -- The Use Case Identification Process Flow Educate Everyone as to How AI/ML Can Solve Business Problems -- Define Your Business Objectives -- Identify the Pain Points -- Start with Root-Cause Analysis -- Identify the Success Metrics -- Explore the Latest Industry Trends -- Review AI Applications in Various Industries -- Map the Use Case to the Business Problem -- Prioritizing Your Use Cases -- Define the Impact Criteria -- Define the Feasibility Criteria -- Assess the Impact -- Assess the Feasibility -- Prioritize the Use Cases -- Review and Refine the Criteria -- Choose the Right Model -- Use Cases to Choose From -- AI Use Cases for DevOps -- AI for Healthcare and Life Sciences -- AI Enabled Contact Center Use Cases -- Business Metrics Analysis -- Content Moderation -- AI for Financial Services -- Cybersecurity -- Digital Twinning -- Identity Verification -- Intelligent Document Processing -- Intelligent Search -- Machine Translation -- Media Intelligence -- ML Modernization -- ML-Powered Personalization -- Computer Vision -- Personal Protective Equipment -- Generative AI -- Workbook Template: Use Case Identification Sheet -- Summary -- Review Questions -- Answer Key -- Chapter 9 Evaluating AI/ML Platforms and Services -- Benefits and Factors to Consider When Choosing an AI/ML Service -- Benefits of Using Cloud AI/ML Services -- Factors to Consider When Choosing an AI/ML Service -- AWS AI and ML Services -- AI Services -- Amazon SageMaker -- AI Frameworks -- Differences Between Machine Learning Algorithms, Models, and Services -- Core AI Services -- Text and Document Services -- Chatbots: Amazon Lex -- Speech -- Vision Services -- Specialized AI Services -- Business Processing Services -- Kendra for Search -- Code and DevOps -- Industrial Solutions -- Healthcare Solutions -- Machine Learning Services -- Amazon SageMaker -- Amazon SageMaker Canvas -- SageMaker Studio Lab The Google AI/ML Services Stack -- For Data Scientists -- For Developers -- The Microsoft AI/ ML Services Stack -- Azure Applied AI Services -- Azure Cognitive Services -- Azure Machine Learning -- Other Enterprise Cloud AI Platforms -- Dataiku -- DataRobot -- KNIME -- IBM Watson -- Salesforce Einstein AI -- Oracle Cloud AI -- Workbook Template: AI/ML Platform Evaluation Sheet -- Summary -- Review Questions -- Answer Key -- Chapter 10 Launching Your Pilot Project -- Launching Your Pilot -- Planning for Launch -- Recap of the Envision Phase -- Planning for the Machine Learning Project -- Following the Machine Learning Lifecycle -- Business Goal Identification -- Machine Learning Problem Framing -- Data Processing -- Model Development -- Model Deployment -- Model Monitoring -- Workbook Template: AI/ML Pilot Launch Checklist -- Summary -- Review Questions -- Answer Key -- Part IV Building and Governing Your Team -- Chapter 11 Empowering Your People Through Org Change Management -- Succeeding Through a People-centric Approach -- Evolve Your Culture for AI Adoption, Innovation, and Change -- Redesign Your Organization for Agility and Innovation with AI -- Aligning Your Organization Around AI Adoption to Achieve Business Outcomes -- Workbook Template: Org Change Management Plan -- Summary -- Review Questions -- Answer Key -- Note -- Chapter 12 Building Your Team -- Understanding the Roles and Responsibilities in an ML Project -- Build a Cross-Functional Team for AI Transformation -- Adopt Cloud and AI to Transform Current Roles -- Customize Roles to Suit Your Business Goals and Needs -- Workbook Template: Team Building Matrix -- Summary -- Review Questions -- Answer Key -- Part V Setting Up Infrastructure and Managing Operations -- Chapter 13 Setting Up an Enterprise AI Cloud Platform Infrastructure -- Reference Architecture Patterns for Typical Use Cases Customer 360-Degree Architecture |
| ctrlnum | (ZDB-30-PQE)EBC31036205 (ZDB-30-PAD)EBC31036205 (ZDB-89-EBL)EBL31036205 (OCoLC)1415869931 (DE-599)BVBBV049871504 |
| dewey-full | 650.028563 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 650 - Management and auxiliary services |
| dewey-raw | 650.028563 |
| dewey-search | 650.028563 |
| dewey-sort | 3650.028563 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Informatik Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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"><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Cover -- Title Page -- Copyright Page -- Acknowledgments -- About the Author -- About the Technical Editor -- Contents -- Introduction -- How This Book Is Organized -- Who Should Read This Book? -- Data Scientists and AI Teams -- IT Leaders and Teams -- Students and Academia -- Consultants and Advisors -- Business Strategists and Leaders -- C-Level Executives -- Why You Should Read This Book -- Unique Features -- Comprehensive Coverage of All Aspects of Enterprise-wide AI Transformation -- Case Study Approach -- Coverage of All Major Cloud Platforms -- Discussion of Nontechnical Aspects of AI -- Best Practices for MLOps and AI Governance -- Up-to-Date Content -- Hands-on Approach -- Part I Introduction -- Chapter 1 Enterprise Transformation with AI in the Cloud -- Understanding Enterprise AI Transformation -- Why Some 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Consolidation</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Becoming Agile and Scalable by Moving Data Centers Into the Cloud -- Building a Resilient System by Embracing Cloud-Native Principles -- Impact of Cloud-First Thinking on DevOps, Agile Development, and Machine Learning -- Becoming an AI-First Company: From Cloud Adoption to Thrilling Customer Experiences -- Case Study 3: Netflix and the Path Companies Take to Become World-Class -- Cloud and AI Technology: A Game-Changer for Netflix's Business Model and Success -- Cloud Infrastructure and AI Adoption Drives Process Transformation -- Process Transformation Drives Organizational Change -- Workbook Template - AI Case Study -- Summary -- Review Questions -- Answer Key -- Part II Strategizing and Assessing for AI -- Chapter 3 Addressing the Challenges with Enterprise AI -- Challenges Faced by Companies Implementing Enterprise-wide AI -- Business-Related Challenges -- Data- and Model-Related Challenges -- Platform-Related Challenges -- How Digital Natives Tackle AI Adoption -- They Are Willing to Take Risks -- They Have an Advantage in Data Collection and Curation Capabilities -- They Attract Top Talent Through Competitive Compensation and Perks -- Get Ready: AI Transformation Is More Challenging Than Digital Transformation -- Complexities of Skill Sets, Technology, and Infrastructure Integration -- The Importance of Data Infrastructure and Governance -- Change Management to Redefine Work Processes and Employee Mindsets -- Regulatory Concerns: Addressing Bias, Ethical, Privacy, and Accountability Risks -- Choosing Between Smaller PoC Point Solutions and Large-Scale AI Initiatives -- The Challenges of Implementing a Large-Scale AI Initiative -- Navigate the Moving Parts, Stakeholders, and Technical Infrastructure -- Resource Allocation Challenges in Large-Scale AI Initiatives -- Overcome Resistance to Change</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Data Security, Privacy, Ethics, Compliance, and Reputation -- Build a Business Case for Large-Scale AI Initiatives -- Factors to Consider -- Workbook Template: AI Challenges Assessment -- Summary -- Review Questions -- Answer Key -- Chapter 4 Designing AI Systems Responsibly -- The Pillars of Responsible AI -- Robust AI -- Collaborative AI -- Trustworthy AI -- Scalable AI -- Human-centric AI -- Workbook Template: Responsible AI Design Template -- Summary -- Review Questions -- Answer Key -- Chapter 5 Envisioning and Aligning Your AI Strategy -- Step-by-Step Methodology for Enterprise-wide AI -- The Envision Phase -- The Align Phase -- Workbook Template: Vision Alignment Worksheet -- Summary -- Review Questions -- Answer Key -- Chapter 6 Developing an AI Strategy and Portfolio -- Leveraging Your Organizational Capabilities for Competitive Advantage -- Focus Areas to Build Your Competitive Advantage -- Driving Competitive Advantage Through AI -- Initiating Your Strategy and Plan to Kickstart Enterprise AI -- Manage Your AI Strategy, Portfolio, Innovation, Product Lifecycle, and Partnerships -- Define Your AI Strategy to Achieve Business Outcomes -- Prioritize Your Portfolio -- Strategy and Execution Across Phases -- Workbook Template: Business Case and AI Strategy -- Summary -- Review Questions -- Answer Key -- Chapter 7 Managing Strategic Change -- Accelerating Your AI Adoption with Strategic Change Management -- Phase 1: Develop an AI Acceleration Charter and Governance Mechanisms for Your AI Initiative -- Phase 2: Ensure Leadership Alignment -- Phase 3: Create a Change Acceleration Strategy -- Workbook Template: Strategic Change Management Plan -- Summary -- Review Questions -- Answer Key -- Part III Planning and Launching a Pilot Project -- Chapter 8 Identifying Use Cases for Your AI/ML Project -- The Use Case Identification Process Flow</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Educate Everyone as to How AI/ML Can Solve Business Problems -- Define Your Business Objectives -- Identify the Pain Points -- Start with Root-Cause Analysis -- Identify the Success Metrics -- Explore the Latest Industry Trends -- Review AI Applications in Various Industries -- Map the Use Case to the Business Problem -- Prioritizing Your Use Cases -- Define the Impact Criteria -- Define the Feasibility Criteria -- Assess the Impact -- Assess the Feasibility -- Prioritize the Use Cases -- Review and Refine the Criteria -- Choose the Right Model -- Use Cases to Choose From -- AI Use Cases for DevOps -- AI for Healthcare and Life Sciences -- AI Enabled Contact Center Use Cases -- Business Metrics Analysis -- Content Moderation -- AI for Financial Services -- Cybersecurity -- Digital Twinning -- Identity Verification -- Intelligent Document Processing -- Intelligent Search -- Machine Translation -- Media Intelligence -- ML Modernization -- ML-Powered Personalization -- Computer Vision -- Personal Protective Equipment -- Generative AI -- Workbook Template: Use Case Identification Sheet -- Summary -- Review Questions -- Answer Key -- Chapter 9 Evaluating AI/ML Platforms and Services -- Benefits and Factors to Consider When Choosing an AI/ML Service -- Benefits of Using Cloud AI/ML Services -- Factors to Consider When Choosing an AI/ML Service -- AWS AI and ML Services -- AI Services -- Amazon SageMaker -- AI Frameworks -- Differences Between Machine Learning Algorithms, Models, and Services -- Core AI Services -- Text and Document Services -- Chatbots: Amazon Lex -- Speech -- Vision Services -- Specialized AI Services -- Business Processing Services -- Kendra for Search -- Code and DevOps -- Industrial Solutions -- Healthcare Solutions -- Machine Learning Services -- Amazon SageMaker -- Amazon SageMaker Canvas -- SageMaker Studio Lab</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">The Google AI/ML Services Stack -- For Data Scientists -- For Developers -- The Microsoft AI/ ML Services Stack -- Azure Applied AI Services -- Azure Cognitive Services -- Azure Machine Learning -- Other Enterprise Cloud AI Platforms -- Dataiku -- DataRobot -- KNIME -- IBM Watson -- Salesforce Einstein AI -- Oracle Cloud AI -- Workbook Template: AI/ML Platform Evaluation Sheet -- Summary -- Review Questions -- Answer Key -- Chapter 10 Launching Your Pilot Project -- Launching Your Pilot -- Planning for Launch -- Recap of the Envision Phase -- Planning for the Machine Learning Project -- Following the Machine Learning Lifecycle -- Business Goal Identification -- Machine Learning Problem Framing -- Data Processing -- Model Development -- Model Deployment -- Model Monitoring -- Workbook Template: AI/ML Pilot Launch Checklist -- Summary -- Review Questions -- Answer Key -- Part IV Building and Governing Your Team -- Chapter 11 Empowering Your People Through Org Change Management -- Succeeding Through a People-centric Approach -- Evolve Your Culture for AI Adoption, Innovation, and Change -- Redesign Your Organization for Agility and Innovation with AI -- Aligning Your Organization Around AI Adoption to Achieve Business Outcomes -- Workbook Template: Org Change Management Plan -- Summary -- Review Questions -- Answer Key -- Note -- Chapter 12 Building Your Team -- Understanding the Roles and Responsibilities in an ML Project -- Build a Cross-Functional Team for AI Transformation -- Adopt Cloud and AI to Transform Current Roles -- Customize Roles to Suit Your Business Goals and Needs -- Workbook Template: Team Building Matrix -- Summary -- Review Questions -- Answer Key -- Part V Setting Up Infrastructure and Managing Operations -- Chapter 13 Setting Up an Enterprise AI Cloud Platform Infrastructure -- Reference Architecture Patterns for Typical Use Cases</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Customer 360-Degree Architecture</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield 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| id | DE-604.BV049871504 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-11T14:09:30Z |
| institution | BVB |
| isbn | 9781394213078 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035210979 |
| oclc_num | 1415869931 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (527 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | John Wiley & Sons, Incorporated |
| record_format | marc |
| series2 | Tech Today Series |
| spelling | Jay, Rabi Verfasser aut Enterprise AI in the Cloud A Practical Guide to Deploying End-To-End Machine Learning and ChatGPT Solutions 1st ed Newark John Wiley & Sons, Incorporated 2024 ©2024 1 Online-Ressource (527 Seiten) txt rdacontent c rdamedia cr rdacarrier Tech Today Series Description based on publisher supplied metadata and other sources Cover -- Title Page -- Copyright Page -- Acknowledgments -- About the Author -- About the Technical Editor -- Contents -- Introduction -- How This Book Is Organized -- Who Should Read This Book? -- Data Scientists and AI Teams -- IT Leaders and Teams -- Students and Academia -- Consultants and Advisors -- Business Strategists and Leaders -- C-Level Executives -- Why You Should Read This Book -- Unique Features -- Comprehensive Coverage of All Aspects of Enterprise-wide AI Transformation -- Case Study Approach -- Coverage of All Major Cloud Platforms -- Discussion of Nontechnical Aspects of AI -- Best Practices for MLOps and AI Governance -- Up-to-Date Content -- Hands-on Approach -- Part I Introduction -- Chapter 1 Enterprise Transformation with AI in the Cloud -- Understanding Enterprise AI Transformation -- Why Some Companies Succeed at Implementing AI and ML While Others Fail -- Transform Your Company by Integrating AI, ML and Gen AI into Your Business Processes -- Adopt AI-First to Become World-Class -- Importance of an AI-First Strategy -- Prioritize AI and Data Initiatives -- Leveraging Enterprise AI Opportunities -- Enable One-to-One, Personalized, Real-Time Service for Customers at Scale -- Enterprise-wide AI Opportunities -- Growing Industry Adoption of AI -- Workbook Template - Enterprise AI Transformation Checklist -- Summary -- Review Questions -- Answer Key -- Chapter 2 Case Studies of Enterprise AI in the Cloud -- Case Study 1: The U.S. Government and the Power of Humans and Machines Working Together to Solve Problems at Scale -- Revolutionizing Operations Management with AI/ML -- Enabling Solutions for Improved Operations -- Case Study 2: Capital One and How It Became a Leading Technology Organization in a Highly Regulated Environment -- Building Amazing Experiences Due to Data Consolidation Becoming Agile and Scalable by Moving Data Centers Into the Cloud -- Building a Resilient System by Embracing Cloud-Native Principles -- Impact of Cloud-First Thinking on DevOps, Agile Development, and Machine Learning -- Becoming an AI-First Company: From Cloud Adoption to Thrilling Customer Experiences -- Case Study 3: Netflix and the Path Companies Take to Become World-Class -- Cloud and AI Technology: A Game-Changer for Netflix's Business Model and Success -- Cloud Infrastructure and AI Adoption Drives Process Transformation -- Process Transformation Drives Organizational Change -- Workbook Template - AI Case Study -- Summary -- Review Questions -- Answer Key -- Part II Strategizing and Assessing for AI -- Chapter 3 Addressing the Challenges with Enterprise AI -- Challenges Faced by Companies Implementing Enterprise-wide AI -- Business-Related Challenges -- Data- and Model-Related Challenges -- Platform-Related Challenges -- How Digital Natives Tackle AI Adoption -- They Are Willing to Take Risks -- They Have an Advantage in Data Collection and Curation Capabilities -- They Attract Top Talent Through Competitive Compensation and Perks -- Get Ready: AI Transformation Is More Challenging Than Digital Transformation -- Complexities of Skill Sets, Technology, and Infrastructure Integration -- The Importance of Data Infrastructure and Governance -- Change Management to Redefine Work Processes and Employee Mindsets -- Regulatory Concerns: Addressing Bias, Ethical, Privacy, and Accountability Risks -- Choosing Between Smaller PoC Point Solutions and Large-Scale AI Initiatives -- The Challenges of Implementing a Large-Scale AI Initiative -- Navigate the Moving Parts, Stakeholders, and Technical Infrastructure -- Resource Allocation Challenges in Large-Scale AI Initiatives -- Overcome Resistance to Change Data Security, Privacy, Ethics, Compliance, and Reputation -- Build a Business Case for Large-Scale AI Initiatives -- Factors to Consider -- Workbook Template: AI Challenges Assessment -- Summary -- Review Questions -- Answer Key -- Chapter 4 Designing AI Systems Responsibly -- The Pillars of Responsible AI -- Robust AI -- Collaborative AI -- Trustworthy AI -- Scalable AI -- Human-centric AI -- Workbook Template: Responsible AI Design Template -- Summary -- Review Questions -- Answer Key -- Chapter 5 Envisioning and Aligning Your AI Strategy -- Step-by-Step Methodology for Enterprise-wide AI -- The Envision Phase -- The Align Phase -- Workbook Template: Vision Alignment Worksheet -- Summary -- Review Questions -- Answer Key -- Chapter 6 Developing an AI Strategy and Portfolio -- Leveraging Your Organizational Capabilities for Competitive Advantage -- Focus Areas to Build Your Competitive Advantage -- Driving Competitive Advantage Through AI -- Initiating Your Strategy and Plan to Kickstart Enterprise AI -- Manage Your AI Strategy, Portfolio, Innovation, Product Lifecycle, and Partnerships -- Define Your AI Strategy to Achieve Business Outcomes -- Prioritize Your Portfolio -- Strategy and Execution Across Phases -- Workbook Template: Business Case and AI Strategy -- Summary -- Review Questions -- Answer Key -- Chapter 7 Managing Strategic Change -- Accelerating Your AI Adoption with Strategic Change Management -- Phase 1: Develop an AI Acceleration Charter and Governance Mechanisms for Your AI Initiative -- Phase 2: Ensure Leadership Alignment -- Phase 3: Create a Change Acceleration Strategy -- Workbook Template: Strategic Change Management Plan -- Summary -- Review Questions -- Answer Key -- Part III Planning and Launching a Pilot Project -- Chapter 8 Identifying Use Cases for Your AI/ML Project -- The Use Case Identification Process Flow Educate Everyone as to How AI/ML Can Solve Business Problems -- Define Your Business Objectives -- Identify the Pain Points -- Start with Root-Cause Analysis -- Identify the Success Metrics -- Explore the Latest Industry Trends -- Review AI Applications in Various Industries -- Map the Use Case to the Business Problem -- Prioritizing Your Use Cases -- Define the Impact Criteria -- Define the Feasibility Criteria -- Assess the Impact -- Assess the Feasibility -- Prioritize the Use Cases -- Review and Refine the Criteria -- Choose the Right Model -- Use Cases to Choose From -- AI Use Cases for DevOps -- AI for Healthcare and Life Sciences -- AI Enabled Contact Center Use Cases -- Business Metrics Analysis -- Content Moderation -- AI for Financial Services -- Cybersecurity -- Digital Twinning -- Identity Verification -- Intelligent Document Processing -- Intelligent Search -- Machine Translation -- Media Intelligence -- ML Modernization -- ML-Powered Personalization -- Computer Vision -- Personal Protective Equipment -- Generative AI -- Workbook Template: Use Case Identification Sheet -- Summary -- Review Questions -- Answer Key -- Chapter 9 Evaluating AI/ML Platforms and Services -- Benefits and Factors to Consider When Choosing an AI/ML Service -- Benefits of Using Cloud AI/ML Services -- Factors to Consider When Choosing an AI/ML Service -- AWS AI and ML Services -- AI Services -- Amazon SageMaker -- AI Frameworks -- Differences Between Machine Learning Algorithms, Models, and Services -- Core AI Services -- Text and Document Services -- Chatbots: Amazon Lex -- Speech -- Vision Services -- Specialized AI Services -- Business Processing Services -- Kendra for Search -- Code and DevOps -- Industrial Solutions -- Healthcare Solutions -- Machine Learning Services -- Amazon SageMaker -- Amazon SageMaker Canvas -- SageMaker Studio Lab The Google AI/ML Services Stack -- For Data Scientists -- For Developers -- The Microsoft AI/ ML Services Stack -- Azure Applied AI Services -- Azure Cognitive Services -- Azure Machine Learning -- Other Enterprise Cloud AI Platforms -- Dataiku -- DataRobot -- KNIME -- IBM Watson -- Salesforce Einstein AI -- Oracle Cloud AI -- Workbook Template: AI/ML Platform Evaluation Sheet -- Summary -- Review Questions -- Answer Key -- Chapter 10 Launching Your Pilot Project -- Launching Your Pilot -- Planning for Launch -- Recap of the Envision Phase -- Planning for the Machine Learning Project -- Following the Machine Learning Lifecycle -- Business Goal Identification -- Machine Learning Problem Framing -- Data Processing -- Model Development -- Model Deployment -- Model Monitoring -- Workbook Template: AI/ML Pilot Launch Checklist -- Summary -- Review Questions -- Answer Key -- Part IV Building and Governing Your Team -- Chapter 11 Empowering Your People Through Org Change Management -- Succeeding Through a People-centric Approach -- Evolve Your Culture for AI Adoption, Innovation, and Change -- Redesign Your Organization for Agility and Innovation with AI -- Aligning Your Organization Around AI Adoption to Achieve Business Outcomes -- Workbook Template: Org Change Management Plan -- Summary -- Review Questions -- Answer Key -- Note -- Chapter 12 Building Your Team -- Understanding the Roles and Responsibilities in an ML Project -- Build a Cross-Functional Team for AI Transformation -- Adopt Cloud and AI to Transform Current Roles -- Customize Roles to Suit Your Business Goals and Needs -- Workbook Template: Team Building Matrix -- Summary -- Review Questions -- Answer Key -- Part V Setting Up Infrastructure and Managing Operations -- Chapter 13 Setting Up an Enterprise AI Cloud Platform Infrastructure -- Reference Architecture Patterns for Typical Use Cases Customer 360-Degree Architecture Erscheint auch als Druck-Ausgabe Jay, Rabi Enterprise AI in the Cloud Newark : John Wiley & Sons, Incorporated,c2024 9781394213054 |
| spellingShingle | Jay, Rabi Enterprise AI in the Cloud A Practical Guide to Deploying End-To-End Machine Learning and ChatGPT Solutions Cover -- Title Page -- Copyright Page -- Acknowledgments -- About the Author -- About the Technical Editor -- Contents -- Introduction -- How This Book Is Organized -- Who Should Read This Book? -- Data Scientists and AI Teams -- IT Leaders and Teams -- Students and Academia -- Consultants and Advisors -- Business Strategists and Leaders -- C-Level Executives -- Why You Should Read This Book -- Unique Features -- Comprehensive Coverage of All Aspects of Enterprise-wide AI Transformation -- Case Study Approach -- Coverage of All Major Cloud Platforms -- Discussion of Nontechnical Aspects of AI -- Best Practices for MLOps and AI Governance -- Up-to-Date Content -- Hands-on Approach -- Part I Introduction -- Chapter 1 Enterprise Transformation with AI in the Cloud -- Understanding Enterprise AI Transformation -- Why Some Companies Succeed at Implementing AI and ML While Others Fail -- Transform Your Company by Integrating AI, ML and Gen AI into Your Business Processes -- Adopt AI-First to Become World-Class -- Importance of an AI-First Strategy -- Prioritize AI and Data Initiatives -- Leveraging Enterprise AI Opportunities -- Enable One-to-One, Personalized, Real-Time Service for Customers at Scale -- Enterprise-wide AI Opportunities -- Growing Industry Adoption of AI -- Workbook Template - Enterprise AI Transformation Checklist -- Summary -- Review Questions -- Answer Key -- Chapter 2 Case Studies of Enterprise AI in the Cloud -- Case Study 1: The U.S. Government and the Power of Humans and Machines Working Together to Solve Problems at Scale -- Revolutionizing Operations Management with AI/ML -- Enabling Solutions for Improved Operations -- Case Study 2: Capital One and How It Became a Leading Technology Organization in a Highly Regulated Environment -- Building Amazing Experiences Due to Data Consolidation Becoming Agile and Scalable by Moving Data Centers Into the Cloud -- Building a Resilient System by Embracing Cloud-Native Principles -- Impact of Cloud-First Thinking on DevOps, Agile Development, and Machine Learning -- Becoming an AI-First Company: From Cloud Adoption to Thrilling Customer Experiences -- Case Study 3: Netflix and the Path Companies Take to Become World-Class -- Cloud and AI Technology: A Game-Changer for Netflix's Business Model and Success -- Cloud Infrastructure and AI Adoption Drives Process Transformation -- Process Transformation Drives Organizational Change -- Workbook Template - AI Case Study -- Summary -- Review Questions -- Answer Key -- Part II Strategizing and Assessing for AI -- Chapter 3 Addressing the Challenges with Enterprise AI -- Challenges Faced by Companies Implementing Enterprise-wide AI -- Business-Related Challenges -- Data- and Model-Related Challenges -- Platform-Related Challenges -- How Digital Natives Tackle AI Adoption -- They Are Willing to Take Risks -- They Have an Advantage in Data Collection and Curation Capabilities -- They Attract Top Talent Through Competitive Compensation and Perks -- Get Ready: AI Transformation Is More Challenging Than Digital Transformation -- Complexities of Skill Sets, Technology, and Infrastructure Integration -- The Importance of Data Infrastructure and Governance -- Change Management to Redefine Work Processes and Employee Mindsets -- Regulatory Concerns: Addressing Bias, Ethical, Privacy, and Accountability Risks -- Choosing Between Smaller PoC Point Solutions and Large-Scale AI Initiatives -- The Challenges of Implementing a Large-Scale AI Initiative -- Navigate the Moving Parts, Stakeholders, and Technical Infrastructure -- Resource Allocation Challenges in Large-Scale AI Initiatives -- Overcome Resistance to Change Data Security, Privacy, Ethics, Compliance, and Reputation -- Build a Business Case for Large-Scale AI Initiatives -- Factors to Consider -- Workbook Template: AI Challenges Assessment -- Summary -- Review Questions -- Answer Key -- Chapter 4 Designing AI Systems Responsibly -- The Pillars of Responsible AI -- Robust AI -- Collaborative AI -- Trustworthy AI -- Scalable AI -- Human-centric AI -- Workbook Template: Responsible AI Design Template -- Summary -- Review Questions -- Answer Key -- Chapter 5 Envisioning and Aligning Your AI Strategy -- Step-by-Step Methodology for Enterprise-wide AI -- The Envision Phase -- The Align Phase -- Workbook Template: Vision Alignment Worksheet -- Summary -- Review Questions -- Answer Key -- Chapter 6 Developing an AI Strategy and Portfolio -- Leveraging Your Organizational Capabilities for Competitive Advantage -- Focus Areas to Build Your Competitive Advantage -- Driving Competitive Advantage Through AI -- Initiating Your Strategy and Plan to Kickstart Enterprise AI -- Manage Your AI Strategy, Portfolio, Innovation, Product Lifecycle, and Partnerships -- Define Your AI Strategy to Achieve Business Outcomes -- Prioritize Your Portfolio -- Strategy and Execution Across Phases -- Workbook Template: Business Case and AI Strategy -- Summary -- Review Questions -- Answer Key -- Chapter 7 Managing Strategic Change -- Accelerating Your AI Adoption with Strategic Change Management -- Phase 1: Develop an AI Acceleration Charter and Governance Mechanisms for Your AI Initiative -- Phase 2: Ensure Leadership Alignment -- Phase 3: Create a Change Acceleration Strategy -- Workbook Template: Strategic Change Management Plan -- Summary -- Review Questions -- Answer Key -- Part III Planning and Launching a Pilot Project -- Chapter 8 Identifying Use Cases for Your AI/ML Project -- The Use Case Identification Process Flow Educate Everyone as to How AI/ML Can Solve Business Problems -- Define Your Business Objectives -- Identify the Pain Points -- Start with Root-Cause Analysis -- Identify the Success Metrics -- Explore the Latest Industry Trends -- Review AI Applications in Various Industries -- Map the Use Case to the Business Problem -- Prioritizing Your Use Cases -- Define the Impact Criteria -- Define the Feasibility Criteria -- Assess the Impact -- Assess the Feasibility -- Prioritize the Use Cases -- Review and Refine the Criteria -- Choose the Right Model -- Use Cases to Choose From -- AI Use Cases for DevOps -- AI for Healthcare and Life Sciences -- AI Enabled Contact Center Use Cases -- Business Metrics Analysis -- Content Moderation -- AI for Financial Services -- Cybersecurity -- Digital Twinning -- Identity Verification -- Intelligent Document Processing -- Intelligent Search -- Machine Translation -- Media Intelligence -- ML Modernization -- ML-Powered Personalization -- Computer Vision -- Personal Protective Equipment -- Generative AI -- Workbook Template: Use Case Identification Sheet -- Summary -- Review Questions -- Answer Key -- Chapter 9 Evaluating AI/ML Platforms and Services -- Benefits and Factors to Consider When Choosing an AI/ML Service -- Benefits of Using Cloud AI/ML Services -- Factors to Consider When Choosing an AI/ML Service -- AWS AI and ML Services -- AI Services -- Amazon SageMaker -- AI Frameworks -- Differences Between Machine Learning Algorithms, Models, and Services -- Core AI Services -- Text and Document Services -- Chatbots: Amazon Lex -- Speech -- Vision Services -- Specialized AI Services -- Business Processing Services -- Kendra for Search -- Code and DevOps -- Industrial Solutions -- Healthcare Solutions -- Machine Learning Services -- Amazon SageMaker -- Amazon SageMaker Canvas -- SageMaker Studio Lab The Google AI/ML Services Stack -- For Data Scientists -- For Developers -- The Microsoft AI/ ML Services Stack -- Azure Applied AI Services -- Azure Cognitive Services -- Azure Machine Learning -- Other Enterprise Cloud AI Platforms -- Dataiku -- DataRobot -- KNIME -- IBM Watson -- Salesforce Einstein AI -- Oracle Cloud AI -- Workbook Template: AI/ML Platform Evaluation Sheet -- Summary -- Review Questions -- Answer Key -- Chapter 10 Launching Your Pilot Project -- Launching Your Pilot -- Planning for Launch -- Recap of the Envision Phase -- Planning for the Machine Learning Project -- Following the Machine Learning Lifecycle -- Business Goal Identification -- Machine Learning Problem Framing -- Data Processing -- Model Development -- Model Deployment -- Model Monitoring -- Workbook Template: AI/ML Pilot Launch Checklist -- Summary -- Review Questions -- Answer Key -- Part IV Building and Governing Your Team -- Chapter 11 Empowering Your People Through Org Change Management -- Succeeding Through a People-centric Approach -- Evolve Your Culture for AI Adoption, Innovation, and Change -- Redesign Your Organization for Agility and Innovation with AI -- Aligning Your Organization Around AI Adoption to Achieve Business Outcomes -- Workbook Template: Org Change Management Plan -- Summary -- Review Questions -- Answer Key -- Note -- Chapter 12 Building Your Team -- Understanding the Roles and Responsibilities in an ML Project -- Build a Cross-Functional Team for AI Transformation -- Adopt Cloud and AI to Transform Current Roles -- Customize Roles to Suit Your Business Goals and Needs -- Workbook Template: Team Building Matrix -- Summary -- Review Questions -- Answer Key -- Part V Setting Up Infrastructure and Managing Operations -- Chapter 13 Setting Up an Enterprise AI Cloud Platform Infrastructure -- Reference Architecture Patterns for Typical Use Cases Customer 360-Degree Architecture |
| title | Enterprise AI in the Cloud A Practical Guide to Deploying End-To-End Machine Learning and ChatGPT Solutions |
| title_auth | Enterprise AI in the Cloud A Practical Guide to Deploying End-To-End Machine Learning and ChatGPT Solutions |
| title_exact_search | Enterprise AI in the Cloud A Practical Guide to Deploying End-To-End Machine Learning and ChatGPT Solutions |
| title_full | Enterprise AI in the Cloud A Practical Guide to Deploying End-To-End Machine Learning and ChatGPT Solutions |
| title_fullStr | Enterprise AI in the Cloud A Practical Guide to Deploying End-To-End Machine Learning and ChatGPT Solutions |
| title_full_unstemmed | Enterprise AI in the Cloud A Practical Guide to Deploying End-To-End Machine Learning and ChatGPT Solutions |
| title_short | Enterprise AI in the Cloud |
| title_sort | enterprise ai in the cloud a practical guide to deploying end to end machine learning and chatgpt solutions |
| title_sub | A Practical Guide to Deploying End-To-End Machine Learning and ChatGPT Solutions |
| work_keys_str_mv | AT jayrabi enterpriseaiinthecloudapracticalguidetodeployingendtoendmachinelearningandchatgptsolutions |