Beginner’s Roadmap to Learning Artificial Intelligence on AWS
Source: Dev.to
Start with Core Foundations (Don’t Skip This Layer)
Before touching AI tools, build your technical backbone.
Key skills to develop
- Python programming (functions, OOP, libraries like NumPy, Pandas)
- Basic data structures and logic building
- Working with APIs and JSON
Why this matters
AI is not magic—it’s code plus data. Without programming fluency, AWS services become black boxes.
Understand Cloud Fundamentals (AWS Basics)
AI on AWS is not separate from the cloud—it lives inside it.
Focus areas
- Core services: EC2, S3, IAM, Lambda
- Regions, availability zones, and pricing basics
- Security fundamentals (roles, policies, permissions)
Strategic move
Start with the AWS Certified Cloud Practitioner to build a structured understanding.
Learn the Basics of AI & Machine Learning
You don’t need to become a data scientist—but you must understand the language.
Core concepts
- Supervised vs. Unsupervised Learning
- Classification vs. Regression
- Model training, validation, and evaluation
- Basics of NLP and Computer Vision
Reality check
Without this layer, you’ll use AI services blindly—and misuse them often.
Start with AWS AI Services (Low‑Code First)
AWS gives you powerful pre‑built AI services—use them before diving deep into model building.
Beginner‑friendly services
- Rekognition – image/video analysis
- Comprehend – NLP and sentiment analysis
- Polly – text‑to‑speech
- Transcribe – speech‑to‑text
Why this works
You learn application‑first AI—how to solve problems, not just train models.
Move to Machine Learning with Amazon SageMaker
Now comes the real engineering layer.
With Amazon SageMaker you will
- Build, train, and deploy ML models
- Work with datasets and feature engineering
- Use notebooks for experimentation
- Deploy models as APIs
Key shift
You move from using AI → building AI systems.