Prerequisites
There are no prerequisites for this training.
What you’ll learn in this course
The Cisco AI Technical Practitioner (AITECH) training is designed for technical professionals seeking to transition from traditional knowledge-based work to innovation-driven roles augmented by Artificial Intelligence (AI). This comprehensive program equips you with the skills to effectively design technical solutions, automate tasks, and lead technical teams using cutting-edge AI tools and methodologies. From AI-powered code generation and data analysis to advanced model customization and workflow automation,
this training prepares IT and network engineers, data analysts, AIOPs specialists, solutions architects, technical leads, managers, and business process analysts to harness the full potential of AI within their organizations.
This training prepares you for the 810-110 AITECH v1.0 exam. If passed, you earn the AI Technical Practitioner certification. This training also earns you 8 Continuing Education (CE) credits toward recertification.
Course Objectives
•Describe common Generative AI models, tools, and practical workflows
•Apply a strategic framework to build a professional AI toolkit by evaluatingplatforms for enterprise readiness, analyzing AI service economics, andmaking the architectural decision between cloud and local deployment
•Explain the importance of effective prompts and apply basic techniques tocraft and refine prompts for improved Generative AI outputs
•Develop multimodal business assets by utilizing generative AI tools tocreate and refine text, visual, and audio content
•Apply security frameworks and governance practices to mitigate datasetbias, protect sensitive data, and neutralize AI-specific threats
•Validate AI-generated outputs by identifying quality issues and biases, andapplying specific techniques to correct those errors for professional use
•Construct complex, multi-step prompts by applying advancedmethodologies 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 ofsecure API usage
•Evaluate the impact of AI on software engineering workflows by analyzingits role in optimizing code quality, velocity, and lifecycle management
•Conduct exploratory data analysis and transformation by utilizinggenerative 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 ofautonomous agentic systems
Course Outline
•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 AI
Further information
If you would like to know more about this course please contact us
