Managing AI in the Enterprise: Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berkeley, CA
Apress L. P.
2021
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| Schlagworte: | |
| Online-Zugang: | HWR01 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (223 Seiten) |
| ISBN: | 9781484278246 |
Internformat
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| 245 | 1 | 0 | |a Managing AI in the Enterprise |b Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations |
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| 264 | 4 | |c ©2022 | |
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| 505 | 8 | |a Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Why Organizations Invest in AI -- The Role of AI in Data-Driven Companies -- Calculating the Business Value of AI -- Use Case: Sales Operations Efficiency -- How and Why Up-Selling, Cross-Selling, Churn Use Cases Thrive -- Hurdles and Prerequisites for Success -- Organizational Impact -- Insights for Product Strategies -- Identifying the Business Challenge -- AI and Analytics vs. Excel -- Understanding the AI Project Deliverables and Methodology -- AI-Driven Innovation in Fashion -- Business Intelligence and AI -- Summary -- Chapter 2: Structuring and Delivering AI Projects -- The Four Layers of Innovation -- Scoping AI Projects -- Understanding the Business Goal -- Understand the Insights Category -- "Prediction Only" vs. "Prediction and Explanation" -- The Training Set Challenge -- Model Update and Usage Frequency -- Identify the Suitable AI Layer -- From Scoping Questions to Business Cases -- Understanding AI Models -- Statistics-Based Models -- Neural Networks -- Advanced Neural Network Topologies -- Developing AI Models -- The Jupyter Notebook Phenomenon -- CRISP-DM for Model Development -- Improving the Productivity of Data Scientists -- Integrating AI Models in IT Solutions -- Summary -- Chapter 3: Quality Assurance in and for AI -- AI Model Quality Metrics -- Performance Metrics for Classification -- Classification and Scoring -- Additional Performance Metrics -- QA Stages in AI Model Engineering -- Perfect but Worthless Model Metrics -- The Training, Validation, and Test Data Split -- Assessing the AI Model with the Training Dataset -- Assessing the AI Model with the Validation Dataset -- Assessing the AI Model with the Test Dataset -- Monitoring AI Models in Production -- Data Quality -- Technical Correctness | |
| 505 | 8 | |a Data Matches Reality? -- Reputation of Data -- QA for AI-Driven Solutions -- Summary -- Chapter 4: Ethics, Regulations, and Explainability -- AI Ethics -- The Three Areas of Ethical Risks -- Handling Ethical Dilemmas -- On Ethical AI Models -- AI Ethics Governance -- AI and Regulations -- Data Privacy Laws: The GDPR Example -- The EU's "AI Act" Proposal -- The Federal Trade Commission's Approach in the US -- Explainable AI -- Scenarios for XAI -- Local Explainability -- Global Explainability -- Summary -- Chapter 5: Building an AI Delivery Organization -- Shaping an AI Service -- IT Services Characteristics -- AI Service Types -- Characterizing AI Service Types -- Understanding Service Attributes -- Designing (for) and Measuring Service Quality -- Managing AI Project Services -- The Capabilities Triumvirate for AI Project Services -- Workload Pattern -- Budgets and Costs -- Selling Results: Data Story Telling -- Managing AI Operations Services -- The Six AI Capabilities -- Workload Pattern -- Understanding and Managing Costs Drivers -- Model Management -- Organizing an AI Organization -- Summary -- Chapter 6: AI and Data Management Architectures -- Architecting AI Environments -- Ingestion Data into AI Environments -- Storing Training Data -- Data Lakes vs. Data Warehouses -- Data Catalogs -- Model and Code Repositories -- Executing AI Models -- AI and Data Management Architectures -- AI and Classic Data Warehouse Architectures -- Self-Service Business Intelligence -- Pantheistic Intelligence -- New Data Categories -- Cloud Services and AI Architecture -- Summary -- Chapter 7: Securing and Protecting AI Environments -- The CIA Triangle -- Security-Related Responsibilities -- Mapping the Risk Landscape -- Threat Actors -- Assets in AI Organizations -- Confidentiality Threats -- Integrity Threats -- Availability Threats | |
| 505 | 8 | |a From Threats to Risks and Mitigation -- Securing AI-Related Systems -- System Hardening -- Governance -- Data Compartmentalization and Access Management -- Advanced Techniques for Sensitive Attributes -- Probing Detection -- Cloud-AI Risk Mitigation -- The ISO 27000 Information Security Standard -- Summary -- Chapter 8: Looking Forward -- Index | |
| 650 | 4 | |a Artificial intelligence-Industrial applications-Congresses | |
| 650 | 4 | |a Business-Data processing | |
| 650 | 4 | |a Machine learning | |
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Datensatz im Suchindex
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| contents | Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Why Organizations Invest in AI -- The Role of AI in Data-Driven Companies -- Calculating the Business Value of AI -- Use Case: Sales Operations Efficiency -- How and Why Up-Selling, Cross-Selling, Churn Use Cases Thrive -- Hurdles and Prerequisites for Success -- Organizational Impact -- Insights for Product Strategies -- Identifying the Business Challenge -- AI and Analytics vs. Excel -- Understanding the AI Project Deliverables and Methodology -- AI-Driven Innovation in Fashion -- Business Intelligence and AI -- Summary -- Chapter 2: Structuring and Delivering AI Projects -- The Four Layers of Innovation -- Scoping AI Projects -- Understanding the Business Goal -- Understand the Insights Category -- "Prediction Only" vs. "Prediction and Explanation" -- The Training Set Challenge -- Model Update and Usage Frequency -- Identify the Suitable AI Layer -- From Scoping Questions to Business Cases -- Understanding AI Models -- Statistics-Based Models -- Neural Networks -- Advanced Neural Network Topologies -- Developing AI Models -- The Jupyter Notebook Phenomenon -- CRISP-DM for Model Development -- Improving the Productivity of Data Scientists -- Integrating AI Models in IT Solutions -- Summary -- Chapter 3: Quality Assurance in and for AI -- AI Model Quality Metrics -- Performance Metrics for Classification -- Classification and Scoring -- Additional Performance Metrics -- QA Stages in AI Model Engineering -- Perfect but Worthless Model Metrics -- The Training, Validation, and Test Data Split -- Assessing the AI Model with the Training Dataset -- Assessing the AI Model with the Validation Dataset -- Assessing the AI Model with the Test Dataset -- Monitoring AI Models in Production -- Data Quality -- Technical Correctness Data Matches Reality? -- Reputation of Data -- QA for AI-Driven Solutions -- Summary -- Chapter 4: Ethics, Regulations, and Explainability -- AI Ethics -- The Three Areas of Ethical Risks -- Handling Ethical Dilemmas -- On Ethical AI Models -- AI Ethics Governance -- AI and Regulations -- Data Privacy Laws: The GDPR Example -- The EU's "AI Act" Proposal -- The Federal Trade Commission's Approach in the US -- Explainable AI -- Scenarios for XAI -- Local Explainability -- Global Explainability -- Summary -- Chapter 5: Building an AI Delivery Organization -- Shaping an AI Service -- IT Services Characteristics -- AI Service Types -- Characterizing AI Service Types -- Understanding Service Attributes -- Designing (for) and Measuring Service Quality -- Managing AI Project Services -- The Capabilities Triumvirate for AI Project Services -- Workload Pattern -- Budgets and Costs -- Selling Results: Data Story Telling -- Managing AI Operations Services -- The Six AI Capabilities -- Workload Pattern -- Understanding and Managing Costs Drivers -- Model Management -- Organizing an AI Organization -- Summary -- Chapter 6: AI and Data Management Architectures -- Architecting AI Environments -- Ingestion Data into AI Environments -- Storing Training Data -- Data Lakes vs. Data Warehouses -- Data Catalogs -- Model and Code Repositories -- Executing AI Models -- AI and Data Management Architectures -- AI and Classic Data Warehouse Architectures -- Self-Service Business Intelligence -- Pantheistic Intelligence -- New Data Categories -- Cloud Services and AI Architecture -- Summary -- Chapter 7: Securing and Protecting AI Environments -- The CIA Triangle -- Security-Related Responsibilities -- Mapping the Risk Landscape -- Threat Actors -- Assets in AI Organizations -- Confidentiality Threats -- Integrity Threats -- Availability Threats From Threats to Risks and Mitigation -- Securing AI-Related Systems -- System Hardening -- Governance -- Data Compartmentalization and Access Management -- Advanced Techniques for Sensitive Attributes -- Probing Detection -- Cloud-AI Risk Mitigation -- The ISO 27000 Information Security Standard -- Summary -- Chapter 8: Looking Forward -- Index |
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| dewey-full | 658.0563 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.0563 |
| dewey-search | 658.0563 |
| dewey-sort | 3658.0563 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Electronic eBook |
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| id | DE-604.BV048830721 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:35:28Z |
| indexdate | 2024-07-10T09:47:11Z |
| institution | BVB |
| isbn | 9781484278246 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034096299 |
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| publishDate | 2021 |
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| spelling | Haller, Klaus Verfasser aut Managing AI in the Enterprise Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations Berkeley, CA Apress L. P. 2021 ©2022 1 Online-Ressource (223 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Why Organizations Invest in AI -- The Role of AI in Data-Driven Companies -- Calculating the Business Value of AI -- Use Case: Sales Operations Efficiency -- How and Why Up-Selling, Cross-Selling, Churn Use Cases Thrive -- Hurdles and Prerequisites for Success -- Organizational Impact -- Insights for Product Strategies -- Identifying the Business Challenge -- AI and Analytics vs. Excel -- Understanding the AI Project Deliverables and Methodology -- AI-Driven Innovation in Fashion -- Business Intelligence and AI -- Summary -- Chapter 2: Structuring and Delivering AI Projects -- The Four Layers of Innovation -- Scoping AI Projects -- Understanding the Business Goal -- Understand the Insights Category -- "Prediction Only" vs. "Prediction and Explanation" -- The Training Set Challenge -- Model Update and Usage Frequency -- Identify the Suitable AI Layer -- From Scoping Questions to Business Cases -- Understanding AI Models -- Statistics-Based Models -- Neural Networks -- Advanced Neural Network Topologies -- Developing AI Models -- The Jupyter Notebook Phenomenon -- CRISP-DM for Model Development -- Improving the Productivity of Data Scientists -- Integrating AI Models in IT Solutions -- Summary -- Chapter 3: Quality Assurance in and for AI -- AI Model Quality Metrics -- Performance Metrics for Classification -- Classification and Scoring -- Additional Performance Metrics -- QA Stages in AI Model Engineering -- Perfect but Worthless Model Metrics -- The Training, Validation, and Test Data Split -- Assessing the AI Model with the Training Dataset -- Assessing the AI Model with the Validation Dataset -- Assessing the AI Model with the Test Dataset -- Monitoring AI Models in Production -- Data Quality -- Technical Correctness Data Matches Reality? -- Reputation of Data -- QA for AI-Driven Solutions -- Summary -- Chapter 4: Ethics, Regulations, and Explainability -- AI Ethics -- The Three Areas of Ethical Risks -- Handling Ethical Dilemmas -- On Ethical AI Models -- AI Ethics Governance -- AI and Regulations -- Data Privacy Laws: The GDPR Example -- The EU's "AI Act" Proposal -- The Federal Trade Commission's Approach in the US -- Explainable AI -- Scenarios for XAI -- Local Explainability -- Global Explainability -- Summary -- Chapter 5: Building an AI Delivery Organization -- Shaping an AI Service -- IT Services Characteristics -- AI Service Types -- Characterizing AI Service Types -- Understanding Service Attributes -- Designing (for) and Measuring Service Quality -- Managing AI Project Services -- The Capabilities Triumvirate for AI Project Services -- Workload Pattern -- Budgets and Costs -- Selling Results: Data Story Telling -- Managing AI Operations Services -- The Six AI Capabilities -- Workload Pattern -- Understanding and Managing Costs Drivers -- Model Management -- Organizing an AI Organization -- Summary -- Chapter 6: AI and Data Management Architectures -- Architecting AI Environments -- Ingestion Data into AI Environments -- Storing Training Data -- Data Lakes vs. Data Warehouses -- Data Catalogs -- Model and Code Repositories -- Executing AI Models -- AI and Data Management Architectures -- AI and Classic Data Warehouse Architectures -- Self-Service Business Intelligence -- Pantheistic Intelligence -- New Data Categories -- Cloud Services and AI Architecture -- Summary -- Chapter 7: Securing and Protecting AI Environments -- The CIA Triangle -- Security-Related Responsibilities -- Mapping the Risk Landscape -- Threat Actors -- Assets in AI Organizations -- Confidentiality Threats -- Integrity Threats -- Availability Threats From Threats to Risks and Mitigation -- Securing AI-Related Systems -- System Hardening -- Governance -- Data Compartmentalization and Access Management -- Advanced Techniques for Sensitive Attributes -- Probing Detection -- Cloud-AI Risk Mitigation -- The ISO 27000 Information Security Standard -- Summary -- Chapter 8: Looking Forward -- Index Artificial intelligence-Industrial applications-Congresses Business-Data processing Machine learning Erscheint auch als Druck-Ausgabe Haller, Klaus Managing AI in the Enterprise Berkeley, CA : Apress L. P.,c2021 9781484278239 |
| spellingShingle | Haller, Klaus Managing AI in the Enterprise Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Why Organizations Invest in AI -- The Role of AI in Data-Driven Companies -- Calculating the Business Value of AI -- Use Case: Sales Operations Efficiency -- How and Why Up-Selling, Cross-Selling, Churn Use Cases Thrive -- Hurdles and Prerequisites for Success -- Organizational Impact -- Insights for Product Strategies -- Identifying the Business Challenge -- AI and Analytics vs. Excel -- Understanding the AI Project Deliverables and Methodology -- AI-Driven Innovation in Fashion -- Business Intelligence and AI -- Summary -- Chapter 2: Structuring and Delivering AI Projects -- The Four Layers of Innovation -- Scoping AI Projects -- Understanding the Business Goal -- Understand the Insights Category -- "Prediction Only" vs. "Prediction and Explanation" -- The Training Set Challenge -- Model Update and Usage Frequency -- Identify the Suitable AI Layer -- From Scoping Questions to Business Cases -- Understanding AI Models -- Statistics-Based Models -- Neural Networks -- Advanced Neural Network Topologies -- Developing AI Models -- The Jupyter Notebook Phenomenon -- CRISP-DM for Model Development -- Improving the Productivity of Data Scientists -- Integrating AI Models in IT Solutions -- Summary -- Chapter 3: Quality Assurance in and for AI -- AI Model Quality Metrics -- Performance Metrics for Classification -- Classification and Scoring -- Additional Performance Metrics -- QA Stages in AI Model Engineering -- Perfect but Worthless Model Metrics -- The Training, Validation, and Test Data Split -- Assessing the AI Model with the Training Dataset -- Assessing the AI Model with the Validation Dataset -- Assessing the AI Model with the Test Dataset -- Monitoring AI Models in Production -- Data Quality -- Technical Correctness Data Matches Reality? -- Reputation of Data -- QA for AI-Driven Solutions -- Summary -- Chapter 4: Ethics, Regulations, and Explainability -- AI Ethics -- The Three Areas of Ethical Risks -- Handling Ethical Dilemmas -- On Ethical AI Models -- AI Ethics Governance -- AI and Regulations -- Data Privacy Laws: The GDPR Example -- The EU's "AI Act" Proposal -- The Federal Trade Commission's Approach in the US -- Explainable AI -- Scenarios for XAI -- Local Explainability -- Global Explainability -- Summary -- Chapter 5: Building an AI Delivery Organization -- Shaping an AI Service -- IT Services Characteristics -- AI Service Types -- Characterizing AI Service Types -- Understanding Service Attributes -- Designing (for) and Measuring Service Quality -- Managing AI Project Services -- The Capabilities Triumvirate for AI Project Services -- Workload Pattern -- Budgets and Costs -- Selling Results: Data Story Telling -- Managing AI Operations Services -- The Six AI Capabilities -- Workload Pattern -- Understanding and Managing Costs Drivers -- Model Management -- Organizing an AI Organization -- Summary -- Chapter 6: AI and Data Management Architectures -- Architecting AI Environments -- Ingestion Data into AI Environments -- Storing Training Data -- Data Lakes vs. Data Warehouses -- Data Catalogs -- Model and Code Repositories -- Executing AI Models -- AI and Data Management Architectures -- AI and Classic Data Warehouse Architectures -- Self-Service Business Intelligence -- Pantheistic Intelligence -- New Data Categories -- Cloud Services and AI Architecture -- Summary -- Chapter 7: Securing and Protecting AI Environments -- The CIA Triangle -- Security-Related Responsibilities -- Mapping the Risk Landscape -- Threat Actors -- Assets in AI Organizations -- Confidentiality Threats -- Integrity Threats -- Availability Threats From Threats to Risks and Mitigation -- Securing AI-Related Systems -- System Hardening -- Governance -- Data Compartmentalization and Access Management -- Advanced Techniques for Sensitive Attributes -- Probing Detection -- Cloud-AI Risk Mitigation -- The ISO 27000 Information Security Standard -- Summary -- Chapter 8: Looking Forward -- Index Artificial intelligence-Industrial applications-Congresses Business-Data processing Machine learning |
| title | Managing AI in the Enterprise Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations |
| title_auth | Managing AI in the Enterprise Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations |
| title_exact_search | Managing AI in the Enterprise Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations |
| title_exact_search_txtP | Managing AI in the Enterprise Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations |
| title_full | Managing AI in the Enterprise Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations |
| title_fullStr | Managing AI in the Enterprise Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations |
| title_full_unstemmed | Managing AI in the Enterprise Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations |
| title_short | Managing AI in the Enterprise |
| title_sort | managing ai in the enterprise succeeding with ai projects and mlops to build sustainable ai organizations |
| title_sub | Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations |
| topic | Artificial intelligence-Industrial applications-Congresses Business-Data processing Machine learning |
| topic_facet | Artificial intelligence-Industrial applications-Congresses Business-Data processing Machine learning |
| work_keys_str_mv | AT hallerklaus managingaiintheenterprisesucceedingwithaiprojectsandmlopstobuildsustainableaiorganizations |