AITECHCICiscoCI-AITECH1.0<ul> <li>Describe common Generative AI models, tools, and practical workflows</li><li>Apply a strategic framework to build a professional AI toolkit by evaluating platforms for enterprise readiness, analyzing AI service economics, and making the architectural decision between cloud and local deployment</li><li>Explain the importance of effective prompts and apply basic techniques to craft and refine prompts for improved Generative AI outputs</li><li>Develop multimodal business assets by utilizing generative AI tools to create and refine text, visual, and audio content</li><li>Apply security frameworks and governance practices to mitigate dataset bias, protect sensitive data, and neutralize AI-specific threats</li><li>Validate AI-generated outputs by identifying quality issues and biases, and applying specific techniques to correct those errors for professional use</li><li>Construct complex, multi-step prompts by applying advanced methodologies to manage ambiguity and elicit specific LLM responses</li><li>Apply generative AI tools to conduct research and synthesize information, and use AI as a catalyst for brainstorming</li><li>Explain the fundamental role of APIs in AI systems and the principles of secure API usage</li><li>Evaluate the impact of AI on software engineering workflows by analyzing its role in optimizing code quality, velocity, and lifecycle management</li><li>Conduct exploratory data analysis and transformation by utilizing generative AI tools to clean datasets and generative insights</li><li>Evaluate AI model customization strategies by differentiating between fine-tuning and RAG and analyzing local deployment architectures</li><li>Design directive AI-powered workflows and describe the architecture of autonomous agentic systems</li></ul><p>There are no prerequisites for this training.</p><ul> <li>IT and Network Engineers</li><li>Data Analysts</li><li>AIOPs Specialists</li><li>Solutions Architects</li><li>Technical Leads</li><li>Managers</li><li>Business Process Analysts</li></ul><ul> <li>Generative AI Ecosystem</li><li>AI Architect&rsquo;s Toolkit</li><li>Prompt Engineering for Technical Precision</li><li>AI-Driven Multimodal Asset Creation</li><li>Generative AI Security and Privacy Fundamentals</li><li>Debugging and Correcting AI-Generated Outputs</li><li>Advanced Prompting Strategies</li><li>AI-Powered Discovery and Synthesis</li><li>AI Systems Integration with APIs</li><li>AI-Driven Software Engineering</li><li>AI for Data Engineering and Exploration</li><li>Customizing AI Models</li><li>AI-Powered Workflows and Agentic AI</li></ul>- Describe common Generative AI models, tools, and practical workflows - Apply a strategic framework to build a professional AI toolkit by evaluating platforms for enterprise readiness, analyzing AI service economics, and making the architectural decision between cloud and local deployment - Explain the importance of effective prompts and apply basic techniques to craft and refine prompts for improved Generative AI outputs - Develop multimodal business assets by utilizing generative AI tools to create and refine text, visual, and audio content - Apply security frameworks and governance practices to mitigate dataset bias, protect sensitive data, and neutralize AI-specific threats - Validate AI-generated outputs by identifying quality issues and biases, and applying specific techniques to correct those errors for professional use - Construct complex, multi-step prompts by applying advanced methodologies to manage ambiguity and elicit specific LLM responses - Apply generative AI tools to conduct research and synthesize information, and use AI as a catalyst for brainstorming - Explain the fundamental role of APIs in AI systems and the principles of secure API usage - Evaluate the impact of AI on software engineering workflows by analyzing its role in optimizing code quality, velocity, and lifecycle management - Conduct exploratory data analysis and transformation by utilizing generative AI tools to clean datasets and generative insights - Evaluate AI model customization strategies by differentiating between fine-tuning and RAG and analyzing local deployment architectures - Design directive AI-powered workflows and describe the architecture of autonomous agentic systemsThere are no prerequisites for this training.- IT and Network Engineers - Data Analysts - AIOPs Specialists - Solutions Architects - Technical Leads - Managers - Business Process Analysts- Generative AI Ecosystem - AI Architect’s Toolkit - Prompt Engineering for Technical Precision - AI-Driven Multimodal Asset Creation - Generative AI Security and Privacy Fundamentals - Debugging and Correcting AI-Generated Outputs - Advanced Prompting Strategies - AI-Powered Discovery and Synthesis - AI Systems Integration with APIs - AI-Driven Software Engineering - AI for Data Engineering and Exploration - Customizing AI Models - AI-Powered Workflows and Agentic AI2 jours1715.001995.002755.0020.0020.0020.00