Name: AWS Artificial Intelligence Practitioner
Price: 174.99 USD

AWS Artificial Intelligence Practitioner

Practitioner literacy, tied to one stack

A short, vendor-grounded course for people who need to be genuinely conversant with AI on AWS — not researchers, but developers, analysts and decision-makers who must pick the right service, stand a model up, and keep it responsible. The trade is breadth for usefulness.

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 actually 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

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
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
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

Generic AI courses can leave you fluent in theory and helpless in front of a console. This one is deliberately bound to a single stack: you cover machine learning and generative AI concepts, then put them to work with Amazon SageMaker and Bedrock, with real attention to prompt engineering, MLOps and responsible AI. Deep on AWS, quiet on everything else — which is the point.

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.