Online
AWS AI practitioner
Duration
12 Hours 25 Minutes
Level
Deep skilling
Delivery
Self-paced online
Assessment
AWS
Certificate
AWS
Overview
Most people now know what AI can do. Far fewer know how to actually build with it inside a cloud environment, or how to tell which of the dozen available services fits the problem in front of them. This learning plan closes that gap.
It starts with the foundations, what AI, machine learning and deep learning are and how they differ, then moves into the AWS AI and ML portfolio: SageMaker for building and deploying models, Rekognition for vision, Comprehend for language, Polly and Transcribe for speech, Lex for conversational interfaces. You learn the model development lifecycle end to end, including the parts people skip, such as data preparation, feature engineering and responsible AI practice.
The content is curated by AWS experts and aligned to roles employers are actually hiring for. It is deliberately accessible: prior coding or cloud experience helps but is not mandatory, which makes it as useful for a business analyst who needs to spot AI opportunities as for a developer who needs to ship them.
What you will learn
By the end of the learning plan you will be able to:
Foundations of AI and ML
- Explain the fundamental concepts of artificial intelligence, machine learning and deep learning
- Differentiate between supervised, unsupervised and reinforcement learning approaches
- Identify real-world applications and use cases of AI across industries
AWS AI and ML services overview
- Understand the AWS AI and ML service portfolio and its capabilities, including Amazon SageMaker, Lex, Polly, Rekognition and Comprehend
- Explore how AWS cloud services integrate AI into scalable business solutions
Model development lifecycle
- Describe the typical steps in building, training and deploying ML models
- Recognise the importance of data collection, preparation and feature engineering
- Understand ethical considerations and responsible AI practices
Hands-on exploration
- Navigate and use key AWS AI services for text, speech, vision and language understanding
- Demonstrate basic workflows in Amazon SageMaker for building and deploying models
- Use AWS AI tools to solve sample business problems
AI for business and decision-making
- Understand how AI drives innovation, automation and data-driven decision-making
- Identify opportunities to integrate AI into workplace scenarios
- Communicate AI concepts to both technical and non-technical stakeholders
Readiness for next steps
- Prepare for advanced AI and ML learning pathways and certifications on AWS
- Build confidence in applying AWS AI services to projects and organisational needs
Prerequisites
The plan caters to participants with basic digital fluency. Prior coding or cloud experience is beneficial but not mandatory. It is designed for:
- Students and early career professionals from technical and non-technical backgrounds building foundational to intermediate AI and ML knowledge
- IT professionals and developers who want to apply AI and ML concepts using AWS services in real projects
- Data analysts and engineers looking to strengthen data-driven decision-making with AI-powered solutions
- Educators and trainers incorporating AI fundamentals and AWS AI tools into their teaching
- Business and functional leaders who want awareness of AI applications to drive innovation and digital strategy
Skills and tools
Skills covered Artificial intelligence fundamentals including applications, industry relevance, AI ethics and responsible AI practice. Machine learning foundations covering supervised, unsupervised and reinforcement learning, data preprocessing, model training, testing and evaluation. Deep learning essentials including neural networks, activation functions and optimisation. Natural language processing covering text analysis, sentiment detection and conversational AI. Computer vision covering image classification and object detection. AI deployment and integration including model serving and API integration.
Tools and platforms AWS AI and ML services: Amazon SageMaker for model building, training and deployment, AWS DeepLens, Amazon Polly for text to speech, Amazon Lex for conversational AI, Amazon Rekognition for image and video analysis, and Amazon Comprehend for NLP and sentiment analysis. Programming and frameworks: Python for AI and ML development, plus TensorFlow and PyTorch basics within the AWS environment. Cloud integration: AWS Management Console and CLI, and cloud-native model deployment practices.
Certificate
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Frequently Asked Questions
Find answers to common questions about the AWS AI Practitioner course.
The AWS AI Practitioner course is a self-paced learning programme that introduces artificial intelligence, machine learning and deep learning concepts within the AWS cloud ecosystem. It also explores practical AWS AI services for building, deploying and integrating AI-powered solutions.
The course is suitable for students, early-career professionals, IT professionals, developers, data analysts, engineers, educators and business professionals who want to understand AI and machine learning and explore their practical applications using AWS services.
You will learn AI, machine learning and deep learning fundamentals, supervised, unsupervised and reinforcement learning, the ML development lifecycle, data preparation, feature engineering, responsible AI and practical applications of AWS AI and ML services.
The learning plan introduces AWS AI and ML services including Amazon SageMaker, Amazon Lex, Amazon Polly, Amazon Rekognition and Amazon Comprehend. Learners explore how these services support machine learning, conversational AI, speech, computer vision and natural language processing applications.
No. Basic digital fluency is sufficient to begin. Previous coding or cloud experience can be helpful, but it is not mandatory. The learning plan is designed for participants from both technical and non-technical backgrounds.
Yes. The course includes practical exploration of AWS AI services for text, speech, vision and language understanding. Learners also explore basic Amazon SageMaker workflows for building and deploying machine learning models and using AWS AI tools for sample business problems.
The programme builds broader AI and machine learning foundations and includes ethical considerations and responsible AI practices. It also prepares learners to understand AI use cases, evaluate opportunities and progress toward more advanced AI and ML learning pathways on AWS.
Yes. The AWS AI Practitioner course is delivered in a self-paced online format, allowing learners to work through the AI and machine learning content according to the structure of the learning plan and their own schedule.
The AWS AI Practitioner learning plan has a duration of approximately 12 hours and 25 minutes. It covers foundational through deeper AI and machine learning concepts along with practical exposure to AWS AI services.
Learners receive a FutureSkills Prime certificate on completion of the learning plan. AWS role-based certifications are separate credentials and are taken separately through AWS. This programme is designed to build foundational knowledge for further AWS AI and ML learning and certification pathways.