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
Start Now
Enroll now.
Gallery
Students speak
