About This Course
The AWS Certified AI Practitioner course is tailored for individuals who want to harness the power of AWS’s AI and machine learning services to build innovative solutions. This course provides a comprehensive guide through the concepts, tools, and best practices needed to design and implement AI systems on AWS, aligning with the certification exam objectives.
Key Learning Objectives
- Understand AWS AI Fundamentals: Get a solid grounding in the core concepts of artificial intelligence and how AWS services support AI solutions.
- Design and Implement AI Solutions: Learn to architect and deploy AI-driven applications using AWS tools such as Amazon SageMaker, Rekognition, Comprehend, Lex, and Polly.
- Integrate AI into Applications: Develop skills in integrating pre-built AI services and custom machine learning models into scalable, secure applications.
- Prepare for Certification: Gain insights into the exam structure, question formats, and strategies to succeed in the AWS Certified AI Practitioner exam.
Exam Domains and Content
The course content is organized around the major domains of the AWS certification exam, ensuring thorough preparation for every exam objective. Key domains include:
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Understanding AWS AI and Machine Learning Services
- Overview of AWS AI services and their practical applications.
- Introduction to core services like Amazon SageMaker, Rekognition, Lex, Comprehend, and Polly.
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Designing AI Solutions on AWS
- Architecting end-to-end AI solutions.
- Best practices for integrating multiple AWS AI services into cohesive solutions.
- Emphasizing scalability, performance, and security.
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Developing and Integrating AI Models
- Hands-on approach to building, training, and deploying machine learning models with Amazon SageMaker.
- Techniques for integrating AI models with business applications.
- Continuous improvement and model management.
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Operationalizing and Monitoring AI Solutions
- Strategies for deploying AI models in production environments.
- Monitoring performance, troubleshooting, and ensuring compliance.
- Managing model updates and scaling solutions.
Learning Objectives
Upon completion of the course, students will be able to:
Confidently design and implement AI solutions using AWS services.
Leverage AWS tools to build, deploy, and maintain machine learning models in production.
Navigate the AWS Certified AI Practitioner exam with a clear understanding of its structure and requirements.
Apply best practices for security, performance, and scalability in AI applications on AWS.
Material Includes
- Hands-on Labs: Interactive sessions using the AWS Management Console and related tools.
- Exam Preparation Materials: Practice tests, detailed study guides, and expert tips for the certification exam.
- Community Forums: Engage with peers and instructors to discuss challenges, share insights, and build a network of AI professionals.
Target Audience
- This course is ideal for:
- AI Engineers and Practitioners looking to deepen their expertise in AWS’s AI and machine learning services.
- Developers and Data Scientists aiming to integrate intelligent capabilities into applications.
- IT Professionals and Architects responsible for designing scalable, secure AI solutions on AWS.
- Business Leaders seeking to understand the impact of AI and drive innovation within their organizations.
Curriculum
18 Lessons
Introduction to AWS AI
Overview of AWS AI and ML services.00:00
Understanding the business value of AI solutions on AWS.00:00
Exam Preparation and Overview
Module 1: AWS AI Services and Fundamentals
Module 2: Designing and Architecting AI Solutions
Module 3: Building, Training, and Deploying AI Models
Module 4: Monitoring, Maintenance, and Security
Module 5: Best Practices and Exam Review
Your Instructors
US$1,500.00
Material Includes
- Hands-on Labs: Interactive sessions using the AWS Management Console and related tools.
- Exam Preparation Materials: Practice tests, detailed study guides, and expert tips for the certification exam.
- Community Forums: Engage with peers and instructors to discuss challenges, share insights, and build a network of AI professionals.
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