GenAI Data Engineering and RAG Systems
Ready to make AI systems work with your organization's unique knowledge and data? Most AI implementations hit a wall because they can't effectively access, process, and utilize enterprise information, leaving vast potential untapped and organizations
Overview
This course includes:
- On-demand videos
- Practice assessments
- Multiple hands-on learning activities
- Exposure to a real-world project
- 100% self-paced learning opportunities
- Certification of completion
Struggling to make AI systems work with your organization's specific knowledge and data? Most AI implementations fail because they can't access and effectively use enterprise information, leaving massive potential untapped.
This course transforms you into a data-driven AI architect who can build sophisticated RAG (Retrieval-Augmented Generation) systems that seamlessly connect AI models with your organization's knowledge base. You'll master the complete data engineering pipeline for AI applications, from preprocessing and vector embeddings to advanced retrieval strategies and performance optimization. Through hands-on implementation, you'll create enterprise-grade document processing systems, intelligent knowledge management platforms, and specialized customer support RAG applications.
By the end of this course, you'll confidently architect data pipelines that power intelligent AI systems, implement RAG solutions that deliver contextually accurate responses, and build knowledge bases that transform how organizations access and utilize their information assets. You'll have the expertise to bridge the gap between raw data and intelligent AI applications.
Join the specialists building the infrastructure that makes AI truly intelligent and become the data engineering expert every AI-driven organization desperately needs.
Skills You Will Gain
Learning Outcomes (At The End Of This Program, You Will Be Able To...)
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Construct robust data processing pipelines that transform raw data into AI-ready formats
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Implement advanced RAG architectures with component integration and performance optimization
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Develop customer support RAG systems with domain-specific knowledge base management
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Apply advanced retrieval strategies including metadata filtering, reranking, and quality enhancement
Prerequisites
Learners should have proficiency in Python, a solid understanding of databases and data processing, basic knowledge of machine learning concepts, and experience working with APIs and web services.
Who Should Attend
This course is designed for data engineers transitioning into AI systems, ML engineers focused on data pipelines, software engineers developing knowledge systems, and AI/ML specialists implementing RAG solutions.
