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Applying GAS/LLM to Transform Business Functions

(CTU-AI360.AA1)
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1

Ethical Application of GAI/LLM in Business Workflows

  • Operationalizing Responsible AI in the Enterprise
  • The Imperative of Responsible AI
  • Hands-On Lab: Experimental Setup
  • Building Governance Frameworks for AI
  • AI Safety and Guardrail Design
  • Regulatory and Governance Landscape
  • Sustainable AI at Scale
  • Responsible AI Implementation Roadmap
  • Future of Responsible AI: Ethical Automation
  • Responsible AI Metrics and Performance Indicators
  • Key Takeaways
  • Reflection Questions
  • The Language of Machines
  • The Core Principles of Prompt Engineering
  • Prompt Engineering in the Enterprise Context
  • Hands-On Lab: Experimental Setup
  • Single-Input Prompting Scenarios
  • Multi-Input Prompting and Scaling
  • Scaling Prompt Engineering Across the Enterprise
  • Prompt Optimization and Automation
  • Ethical and Responsible Prompting
  • Future Trends in Prompt Engineering
  • Key Takeaways
  • Reflection Questions
  • Introduction: From Compliance to Conscious Design
  • The Six Ethical Dimensions of Enterprise AI
  • Responsible Infusion: Embedding Ethics into Enterprise DNA
  • User-Centric Design and Human Alignment
  • Hands-On Lab: Experimental Setup
  • Ethical Guardrails and Governance Metrics
  • Communication and Cultural Adoption
  • Future of Ethical AI in Enterprises
  • Key Takeaways
  • Reflection Questions
2

Multimedia AI Tools for Task Automation and Enhancement

  • Module 1: Overview of Multimedia AI Capabilities
  • Module 2: Modality-Specific Prompting Strategies: Optimizing Inputs for Multimodal AI
  • Module 3: Applied Multimodal AI: Real-World Use Cases
3

Designing AI-Enhanced Workflows and Automation Pipelines

  • Introduction: From Models to Systems
  • The Concept of AI Orchestration
  • Key Objectives:
  • Components of an Orchestration Platform
  • Orchestration Across Deployment Environments
  • Hands-On Lab: Experimental Setup
  • Workflow Design and Automation
  • Model Orchestration Framework
  • Governance and Observability Integration
  • Integration with Enterprise Systems
  • Future of AI Orchestration
  • Key Takeaways
  • Reflection Questions
  • Introduction: From Infrastructure to Intelligence Services
  • What is Model-as-a-Service (MaaS)?
  • Architecture of Model-as-a-Service
  • The MaaS Quadrants: Evaluating Service Models
  • Advantages of the MaaS Model
  • Risks and Challenges
  • MaaS Implementation Framework
  • MaaS and AI Ecosystem Integration
  • Hands-On Lab: Experimental Setup
  • Future of MaaS: Autonomous and Federated Models
  • Key Takeaways
  • Reflection Questions
4

Secure Implementation of AI-Driven Business Transformation

  • Introduction: The Trust Imperative in Enterprise AI
  • What is Confidential AI?
  • Technical Foundations of Confidential AI
  • Vulnerabilities in AI Confidentiality
  • Confidential AI Architecture for Enterprises
  • Confidential AI in Practice: Industry Use Cases
  • Hands-On Lab: Experimental Setup
  • Governance and Compliance in Confidential AI
  • The Future of Confidential AI
  • Key Takeaways
  • Reflection Questions
  • Introduction: From Automation to Autonomy
  • Pillars of the Autonomous Enterprise
  • The Architecture of Autonomous AI Systems
  • Role of Multi-Agent and Multi-Modal Intelligence
  • Ethical Autonomy and Human-AI Co-Governance
  • AI-Driven Business Ecosystems
  • Future Technologies Driving Enterprise AI Evolution
  • The Human Role in an Autonomous AI Future
  • Vision 2035: The Autonomous Intelligent Enterprise
  • Key Takeaways
  • Reflection Questions
5

Measuring and Leading AI-Driven Business Transformation

  • Hands-On Lab: Experimental Setup
  • Challenges of Model-Specific Scaling
  • Model Sourcing and Deployment Strategies
  • Five Dimensions of Model Scale
  • LLMOps: The Operational Backbone Of Enterprise-Scale AI
  • Data Management in Production
  • Integrating Model Governance and Observability
  • Future Trends in Scalable Production
  • Business Objectives of Using Large Language Models (LLMs)
  • Key Takeaways
  • Reflection Questions
  • Understanding the Model Landscape
  • Key Decision Factors for Enterprises
  • Strategic Implications
  • Model Sourcing and Selection
  • Hands-On Lab: Experimental Setup
  • Data Management: The Foundation of AI Performance
  • Model Evaluation, Fine-Tuning, and Optimization
  • Model Orchestration, Observability, and Governance
  • Production-Grade Scaling and Enterprise Readiness
  • Model Observability
  • Model Governance
  • Key Takeaways
  • Reflection Questions
  • Why Latency Matters in Generative AI
  • Understanding Latency in Generative AI
  • Holistic Latency Optimization Framework
  • Balancing Latency, Accuracy, and Cost
  • Hands-On Lab: Experimental Setup
  • Future of Latency Optimization in Generative AI
  • Key Takeaways
  • Reflection Questions

1

Ethical Application of GAI/LLM in Business Workflows

  • Designing and Operationalizing Responsible AI at Enterprise Scale
  • Guiding Model Behavior Through Prompt Design
  • Auditing and Redesigning AI Workflows with Ethical Principles
2

Multimedia AI Tools for Task Automation and Enhancement

  • Extracting Structured Financial Data Using Constrained Prompting
  • Inspecting Visual Details Using Spatially Constrained Prompting
3

Designing AI-Enhanced Workflows and Automation Pipelines

  • Orchestrating Generative AI Systems at Enterprise Scale
  • Evaluating and Adopting Model-as-a-Service in Enterprise AI
4

Secure Implementation of AI-Driven Business Transformation

  • Safeguarding Enterprise Intelligence through Confidential AI
  • Developing a Secure AI Transformation Plan for a Business Function
5

Measuring and Leading AI-Driven Business Transformation

  • Designing Prompts for Scaling and Governing Enterprise Generative AI
  • Measuring and Presenting the Impact of an AI Workflow Redesign
  • Engineering Low-Latency Generative AI Systems

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