AWS Bedrock Agent Development
Learn to build enterprise-grade AI applications using AWS Bedrock. Master MLOps, infrastructure as code, and agent development for generative AI solutions.
Learn to build enterprise-grade AI applications using AWS Bedrock. Master MLOps, infrastructure as code, and agent development for generative AI solutions.
This comprehensive course teaches you to develop enterprise-ready AI applications using AWS Bedrock and Python. You'll gain expertise in MLOps for generative AI, focusing on AWS Bedrock implementation and infrastructure management. The curriculum covers essential topics including Large Language Models (LLMs), Infrastructure as Code using AWS CDK, and data engineering practices. You'll learn to build and optimize Bedrock Agents using AWS Lambda, create intuitive user interfaces, and implement operational dashboards. The course emphasizes hands-on experience with real-world applications, ensuring you master both theoretical concepts and practical implementation. By completion, you'll be equipped to develop, deploy, and manage sophisticated AI solutions in enterprise environments.
Instructors:
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What you'll learn
Master AWS Bedrock implementation and LLM fundamentals
Develop proficiency in Infrastructure as Code using AWS CDK
Build and optimize Bedrock Agents with AWS Lambda
Implement data engineering and pipeline optimization techniques
Create user interfaces and operational dashboards for AI applications
Apply MLOps best practices in enterprise environments
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 4 modules in this course
The course provides comprehensive coverage of MLOps for generative AI using AWS Bedrock. Students learn fundamental concepts of Large Language Models (LLMs) and their practical implementation in enterprise environments. The curriculum includes hands-on experience with AWS cloud infrastructure, data engineering practices, and Bedrock Agent development. Key focus areas include Infrastructure as Code using AWS CDK, Lambda tool integration, user interface design, and operational dashboard creation. The course emphasizes best practices for building trustworthy and valuable AI solutions while providing practical experience with cutting-edge LLM technology.
Introduction to MLOps for LLM
Module 1
IaC and DataOps
Module 2
Building Bedrock Agent
Module 3
Building Interfaces and Operations
Module 4
Fee Structure
Instructor
2 Courses
AI Education Pioneer and Enterprise Transformation Leader
Guy Ernest serves as Chief Engineering Officer at Pragmatic AI Labs, where he focuses on democratizing AI and machine learning education across diverse audiences. His work spans three key areas: training software developers in machine learning engineering using fastai, PyTorch, and Amazon AI services; guiding enterprise leaders through organizational AI transformation; and introducing young learners to AI through hands-on experience with Alexa skills, computer vision, and robotics. His approach combines practical implementation with strategic vision, helping organizations navigate the complexities of AI adoption while building the next generation of AI practitioners. Through summer camps and after-school programs, he makes advanced technologies accessible to children, fostering early interest in AI and machine learning. His expertise in enterprise AI transformation helps businesses understand and implement machine learning solutions effectively, bridging the gap between technical capabilities and business applications.
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