Building RAG Systems with AWS Bedrock

Hosted by Amir Feizpour and Omid Omidi

Fri, May 30, 2025

4:00 PM UTC (45 minutes)

Virtual (Zoom)

Free to join

85 students

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Build Multi-Agent Applications - A Bootcamp
Amir Feizpour, PhD and Abhimanyu Anand
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What you'll learn

How AWS Bedrock simplifies deploying LLMs

Key Bedrock features that support scalable retrieval

Best practices for orchestration prompts in production

Why this topic matters

Retrieval-Augmented Generation (RAG) boosts LLM performance by grounding responses in external data. AWS Bedrock offers a powerful, fully-managed way to build RAG systems without the hassle of managing infrastructure. As demand grows for accurate, explainable AI, understanding Bedrock’s capabilities is crucial for any ML practitioner or builder.

You'll learn from

Amir Feizpour

Founder @ Aggregate Intellect

Amir Feizpour is the founder, CEO, and Chief Scientist at Aggregate Intellect building a generative business brain for service and science based companies. Amir has built and grown a global community of 5000+ AI practitioners and researchers gathered around topics in AI research, engineering, product development, and responsibility. Prior to this, Amir was an NLP Product Lead at Royal Bank of Canada. Amir held a research position at University of Oxford conducting experiments on quantum computing resulting in high profile publications and patents. Amir holds a PhD in Physics from University of Toronto. Amir also serves the AI ecosystem as an advisor at MaRS Discovery District, works with several startups as fractional chief AI officer, and engages with a wide range of community audiences (business executives to hands-on developers) through training and educational programs. Amir leads Aggregate Intellect’s R&D via several academic collaborations.

Omid Omidi

MLE

I’m Abbas (aka Omid), a machine learning engineer with a background in both industry and academia.


I’m a founding member and Head of the AI team at evolo.ai, where I lead the development of business solutions using AI technologies, including large language models (LLMs), retrieval-augmented generation (RAG), and 3D computer vision.


Previously, I worked as a Data Engineer at Holmetrics, where I designed and maintained cloud-based ETL pipelines, managed large-scale datasets, and built scalable, automated data workflows using AWS and modern data infrastructure.


At AltaML, as an Associate Machine Learning Developer, I contributed to machine learning pipelines and automation workflows, developed solid Git practices, and learned the importance of collaboration and effective communication in fast-paced teams.


I also served as a Machine Learning Research Assistant at the University of Calgary, focusing on 3D computer vision and unsupervised domain adaptation for medical imaging.

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