Overview
The Cisco AI Technical Practitioner (AITECH) Digital Learning Path is designed for technical professionals seeking to transition from traditional knowledge-based work to innovation-driven roles augmented by Artificial Intelligence. This comprehensive program equips learners 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 Learning Path 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.
Skills You'll Learn
- Utilize AI for code generation, refactoring, and utilize modern AI-assisted coding workflows (Co-Pilot, Fast Prototyping)
- Leverage Generative AI for exploratory data analysis, data cleaning, transformation, and generating actionable insights
- Design and implement multi-step AI-assisted workflows and understand complex agentic systems for automation
- Understand AI-powered requirements, evaluate customization approaches (fine-tuning vs. RAG), consider deployment strategies, and design robust AI workflows
- Explore, evaluate, fine-tune, and deploy pre-trained AI models, and implement Retrieval Augmented Generation (RAG) systems
- Monitor, maintain, and optimize AI-powered workflows, ensuring data integrity and security
Course Objectives
- AI Technical Practitioner Foundations: Build foundational expertise in Generative AI by exploring core models, applying frameworks, learning prompting, and implementing security best practices.
- Optimizing Generative AI: Advance AI engineering skills by validating outputs, constructing complex prompts, synthesizing research, and analyzing secure API integration.
- Advanced Generative AI Skills: Elevate technical practice by evaluating AI's impact, applying generative tools, customizing models, and designing scalable architectures.