Description
Agentic AI & RAG – Gen AI with AWS Bedrock, Google Vertex AI. This course focuses on mastering the concepts of Generative AI, including RAG architectures and Agentic AI, using AWS Bedrock and Google Vertex AI cloud platform services. Designed for a wide range of learners including students, data scientists, AI and machine learning engineers, developers, and product managers, this course makes it easy for beginners to enter the world of AI by eliminating the need for a Python programming background. The course content starts with teaching the basics of Generative AI and cloud computing, and then moves on to practical projects with AWS Bedrock services such as S3, Lambda, and API Gateway. In this section, participants learn how to build applications, including creating a knowledge base, implementing RAG, and configuring guards to ensure safe and secure outputs. The next part of the course is dedicated to exploring Google Vertex AI capabilities, where participants will learn about agent-based AI for dynamic decision-making and the Vertex AI RAG capability for building intelligent systems, and learn how to integrate with APIs and cloud functions. The course also covers specialized AI offerings such as Amazon Q, SageMaker, and Google services. With comprehensive hands-on exercises and clear explanations, the course ensures that participants will gain the necessary practical skills in the field of productive AI and cloud services, and ultimately be able to build and deploy scalable and powerful AI applications, paving the way for professional advancement in this dynamic field.
What you will learn
- Fundamentals of Generative Artificial Intelligence:
- Understand the core concepts of generative AI, including Prompt Engineering, cloud computing, and how AI models work.
- AWS Bedrock:
- How to set up AWS Bedrock, explore the console, and use services like S3, Lambda, and API Gateway to build AI-powered applications.
- Knowledge Base and RAG (Retrieval-Augmented Generation):
- Build applications that use RAG to interact with custom data stored in S3 using AWS Bedrock.
- Creating Agents and Guardrails::
- Create powerful agents with Guardrails to ensure safe and reliable interactions with AI models.
- Google Vertex AI:
- Review of Google’s Vertex AI offerings, how to set up Vertex AI Studio, and implement RAG and Agentic AI use cases.
- Practical projects:
- Implementing real-world use cases, including building AI knowledge base applications, travel agency applications, and integrating external APIs and cloud functions.
- Cloud Integration:
- How to effectively integrate generative AI with cloud services for scalability, security, and storage.
This course is suitable for people who:
- Beginners in AI and the Cloud:
- People who have no prior experience in Python, AI, or cloud computing and want to gain practical skills in generative AI.
- Students:
- Those studying computer science, artificial intelligence, or data science and want to explore the practical applications of AI and the cloud.
- Data scientists and machine learning engineers:
- Professionals looking to expand their expertise in Generative AI, RAG, and Agentic AI using AWS Bedrock and Google Vertex AI.
- Developers:
- Software developers looking to learn how to build AI-powered applications with cloud integration using AWS and Google Cloud.
- Product Managers:
- Those who manage AI-related products and seek to understand how generative AI and cloud solutions can be used to improve product features.
- Researchers:
- AI researchers who are interested in learning about practical applications of generative AI on cloud platforms and exploring new AI tools.
Course details
- Publisher: Udemy
- Instructor: Soumen Kumar Mondal
- Training level: Beginner to advanced
- Training duration: 5 hours and 23 minutes
- Number of lessons: 33
Course topics
Prerequisites for the Agentic AI & RAG – Gen AI with AWS Bedrock Google Vertex AI course
- No programming or cloud experience required. A desire to learn about Generative AI, cloud services, and how they work together.
Course images
Sample course video
Installation Guide
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Download link
Downloadly
Rapidgator link
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File size
2.8 GB
