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Amazon Bedrock : build AWS GenAI with no code.
- Format:
- Video
- Language:
- English
- Subjects (All):
- Amazon Web Services (Firm).
- Application software--Development.
- Application software.
- Cloud computing.
- Generative artificial intelligence.
- Physical Description:
- 1 online resource (1 video file (4 hr., 50 min.)) : sound, color.
- Edition:
- [First edition].
- Place of Publication:
- [Birmingham, United Kingdom] : Packt Publishing, 2026.
- Summary:
- In this 4-hour course, you will learn how to build and manage generative AI solutions using Amazon Bedrock without writing code. Through practical demonstrations and hands-on exercises, you'll explore foundation models, prompt engineering, knowledge bases, AI agents, and governance features to create secure and scalable GenAI applications on AWS. What I will be able to do after this course Apply prompt engineering and configure inference parameters. Create AI solutions using RAG and knowledge bases. Configure and manage Bedrock Agents and prompt flows. Implement guardrails and responsible AI controls. Evaluate foundation models for different use cases. Course Instructor(s) Karan Gupta is a DevOps Engineer and 4x AWS Certified professional with expertise in cloud infrastructure, Kubernetes, and automation. He has trained over 5,000 students and has extensive experience designing CI/CD pipelines and scalable solutions for startups and enterprises. Karan is also a Certified Kubernetes Administrator and author of multiple research papers focused on technology innovation and societal impact. Who is it for? This course is ideal for business professionals, solution architects, AWS users, AI enthusiasts, technical managers, and analysts who want to build generative AI solutions without coding. It is suited for learners interested in Amazon Bedrock, foundation models, prompt engineering, AI agents, and workflow automation, with only a basic familiarity with AWS concepts recommended.
- Notes:
- OCLC-licensed vendor bibliographic record.
- ISBN:
- 9781808493577
- OCLC:
- 1608633599
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