AWS Fundamentals for Building AI Systems
Aki Wijesundara, PhD
AI Founder | Google AI Accelerator Alum
Manu Jayawardana
AI Advisor | Co-Founder & CEO at Krybe
From AWS AI guesswork to real system-level understanding
Most professionals meet AI on AWS at the surface, calling APIs, testing demos, and deploying notebooks that work until they do not. Latency spikes, costs grow, permissions break, data leaks context, and models fail in production. AWS feels powerful but opaque. This course closes that gap fast by building a clear mental model of AWS as an AI platform with data, models, infrastructure, security, cost, and production working together.
With this course, you’ll:✅ Understand how AWS structures AI systems across regions, services, and infrastructure
✅ Know when to use Bedrock vs SageMaker vs managed AI APIs
✅ Design secure, scalable AI architectures — not just demos
✅ Reason about data flow, model inference, latency, and cost
✅ Apply responsible AI guardrails and governance from day one
✅ Debug AI system failures instead of rebuilding from scratch
Week by week, you move from vague “let’s add AI” ideas to production-ready AI systems on AWS.
You’ll finish able to read AI architecture diagrams, participate in AI design reviews, and contribute meaningfully to real-world AI projects — across product, data, ML, and engineering teams.
The foundation teams need to take AI into production confidently.
What you’ll learn
Build practical AWS AI skills to design, deploy, and operate AI systems with confidence. By the end, you will not just use AWS AI, you will
Understand AWS’s AI stack: infrastructure, data, models, and applications
Choose the right tools for generative AI, ML, and agent-based systems
Deploy inference workloads with performance and reliability in mind
Use Amazon Bedrock and foundation models correctly
Design prompt, context, and retrieval workflows
Understand agentic AI concepts and when to apply them
Apply IAM roles and permissions for AI workloads
Secure data used for training, fine-tuning, and inference
Use guardrails and responsible AI tools effectively
Understand how AI workloads are priced on AWS
Avoid common cost traps in training and inference
Design scalable architectures that grow safely
Who this course is for
Early-career engineers and developers
New to AWS, seeking hands-on skills to deploy, manage, and scale cloud infrastructure confidently.
Data and AI professionals
Work with datasets or ML models and want practical cloud knowledge to run projects efficiently.Technical leads or DevOps newcomers
Guiding teams on cloud adoption and security, aiming to make informed AWS architecture decisions.
Prerequisites
Basic technical familiarity
✅ Comfortable with computers, operating systems (Windows, Linux, or Mac), and navigating online dashboards.
Curiosity to explore cloud systems
✅ Eager to understand and deploy AWS resources — no prior cloud or AWS experience needed.
What's included
Live sessions
Learn directly from Aki Wijesundara, PhD & Manu Jayawardana in a real-time, interactive format.
Hands-on labs
Step-by-step exercises deploying EC2, S3, RDS, and networking components.
Practical projects
Deploy production-ready workloads and build reusable cloud workflows.
Certificate of completion
Earn the “From Zero to Cloud: AWS Fundamentals for Technical Professionals ” certificate to showcase your new skills.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
Week 1
Jan
25
From Zero to Cloud: AWS Fundamentals for Technical Professionals
AWS Fundamentals for Technical Professionals
Schedule
Live sessions
1 hr
Sun, Jan 25
10:00 PM—11:00 PM (UTC)
Projects
1 hr
$349
USD
3 days left to enroll