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AWS Certified AI Practitioner Certification

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The AWS Certified AI Practitioner certification is designed to validate foundational knowledge of Artificial Intelligence (AI) and Machine Learning (ML) in the context of AWS services. It is ideal for individuals aiming to understand how AI/ML solutions can be implemented on AWS for practical and business-driven use cases.

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What is AWS Certified AI Practitioner Certification Training about?

This certification provides an overview of AI/ML fundamentals, AWS AI/ML services, model deployment strategies, ethical considerations, and use cases. It focuses on empowering non-technical professionals and early-career technologists to communicate, evaluate, and contribute to AI initiatives.

Exam Format

  • Type: Multiple Choice, Multiple Response

  • Duration: 90 minutes

  • Number of Questions: ~65

  • Passing Score: ~70%

What are the objectives of AWS Certified AI Practitioner Certification Training ?

By the end of the course and exam prep, learners will be able to:

  • Explain the fundamentals of Artificial Intelligence and Machine Learning
  • Identify AWS services used for AI/ML development (e.g., SageMaker, Rekognition, Polly, Comprehend)
  • Understand the phases of the ML pipeline and when to use them
  • Articulate ethical considerations in AI
  • Describe AI-driven business use cases and deployment strategies

Who is AWS Certified AI Practitioner Certification Training for?

  • Beginners in AI/ML fields
  • Business analysts, project managers, and product owners
  • L&D professionals and training consultants
  • Technical sales and solution consultants
  • Developers or IT professionals new to ML on AWS

What are the prerequisites for AWS Certified AI Practitioner Certification Training?

  • No prior ML experience required
  • Basic understanding of cloud computing recommended
  • Familiarity with AWS basics is helpful but not mandatory
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AWS Certified AI Practitioner Certification Training - Certification & Exam

This course outlines provide comprehensive guide to help candidates demonstrate their understanding of the AWS AI and ML principles.

Module 1: Introduction to AI and Machine Learning

  • History and evolution

  • Key AI/ML concepts

  • AI vs ML vs Deep Learning

Module 2: AWS Cloud Overview for AI/ML

  • AWS core services: EC2, S3, IAM

  • Introduction to AWS ML Stack

  • Setting up the environment

Module 3: AWS AI Services

  • Amazon SageMaker – ML model building and deployment

  • Amazon Rekognition – Image and video analysis

  • Amazon Comprehend – Natural language processing

  • Amazon Lex – Conversational interfaces

  • Amazon Polly – Text-to-speech

Module 4: Machine Learning Lifecycle

  • Data collection and preparation

  • Model training and tuning

  • Model evaluation and deployment

Module 5: AI Use Cases & Applications

  • Real-world case studies

  • AI in customer service, operations, marketing, and logistics

  • ML pipelines in enterprise settings

Module 6: Responsible AI and Ethics

  • Bias in datasets

  • Privacy and fairness

  • Responsible deployment principles