AWS Artificial Intelligence Practitioner

What you will learn

• →Tell AI, machine learning, deep learning and generative AI apart, and place NLP and computer vision within them
• →Match AWS services such as SageMaker and Bedrock to the problems they fit
• →Move a model through the AWS console: prepare data, train, deploy and monitor
• →Judge which AI approach suits a given business scenario, and where its limits are

Curriculum

1. 1Core concepts of AI, ML and generative AI
   • How AI, ML, deep learning and generative AI relate
   • Common algorithms and the data used to train models
   • Use cases across NLP, computer vision and automation
2. 2Building AI solutions with AWS services
   • Amazon SageMaker and Amazon Bedrock
   • Defining use cases, training and evaluating models
   • Lifecycle management with MLOps and fine-tuning
3. 3Responsible, secure and explainable AI
   • Fairness, transparency and human-centred design
   • Data privacy, bias reduction and governance
   • AWS tools for security, compliance and prompt engineering

Prerequisites

• A basic understanding of AWS services
• Basic AI/ML concepts

Concepts this course teaches

• Machine Learning

  Core ML concepts, grounded on AWS tooling.

• Generative AI

  GenAI concepts applied with Amazon Bedrock.

• Prompt Engineering

  Prompting plus responsible-AI practice on AWS.

• Natural Language Processing

  NLP among the surveyed AI use cases.

• Computer Vision

  Computer vision among the surveyed use cases.

FAQ

Is this only useful if my company runs on AWS?

Largely, yes. The hands-on work is AWS-specific. The conceptual half (how AI, ML and generative AI differ, where each fits) transfers anywhere; the tooling half pays off most if SageMaker and Bedrock are in your future.

Do I need to be a developer?

No. It is pitched at developers, analysts and business professionals alike, provided you arrive with a basic grasp of AWS and of AI/ML ideas.