AWS Certified AI Practitioner

Overview

Welcome to the transformative journey that is the AWS Certified AI Practitioner Course! In the rapidly evolving AI landscape, understanding AI concepts is essential, but implementing these concepts on AWS is where the true challenge and opportunity lie.

If integrating AI into AWS feels overwhelming, rest assured, you're not alone. This course is designed to bridge the gap between theoretical knowledge and real-world AWS applications for those already familiar with foundational AWS concepts.

Through practical, scenario-based learning, gain valuable skills to excel in the AWS AI ecosystem, moving beyond the basics with applicable insights.

Moreover, this course prepares you for the AWS Certified AI Practitioner exam, equipping you with the expertise to validate your skills in AI-powered AWS solutions.

Course Modules

1. Fundamentals of AI and ML:

   Understand AI concepts, distinguishing between AI, machine learning, and deep learning. Engage with data types, learning methods, and identify AI and ML use cases, building a robust foundation for AI endeavors on AWS.

2. Fundamentals of Generative AI:

   Explore generative AI's unique aspects, including tokens, embeddings, and foundation models' lifecycle.

   Discuss cost considerations and AWS infrastructure for generative AI, alongside real-world applications, benefits, and limitations.

3. Applications of Foundation Models:

   Learn to design and customize applications using foundation models, from fine-tuning pre-trained models to implementing retrieval-augmented generation and vector databases. Gain insights into effective AI model deployment on AWS with best practices in prompt engineering and evaluating model performance.

4. Guidelines for Responsible AI:

   Examine principles and tools for creating responsible AI applications.

   Discuss model selection, legal risk management, and bias mitigation to ensure safe and ethical AI solutions, anchored in transparent, human-centered design.

5. Security, Compliance, and Governance for AI Solutions:

   Address securing AI systems on AWS with best practices in data engineering, regulatory compliance, and governance strategies, ensuring secure and trustworthy AI applications.

6. Conclusion and Next Steps:

   Summarize key concepts, complete a final assessment, and explore resources for ongoing learning in the dynamic AWS AI/ML space. Reflect on AI's future impact within AWS, preparing for continued advancement in this field.

Equip yourself to master AI on AWS in this hands-on course, where theory meets real-world application complexity.

Whether enhancing your current role or forging new paths in AI, this course is your launchpad into the future of AI on AWS.

University:

LearnQuest

Provider:

Coursera

Categories:

Artificial Intelligence Courses, Machine Learning Courses, Generative AI Courses, MLOps Courses, Amazon Web Services (AWS) Courses, Vector Databases Courses, Responsible AI Courses, Foundation Models Courses, Retrieval Augmented Generation Courses

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