You know you need AI skills.
We built the plan.
Most engineers spend months watching videos and reading docs without building anything they can show. Confident Prep gives you a structured path of real, deployable AI projects — matched to where you are and where you want to go.
Written and curated by practising engineers. No theory dumps. No padding. Every project ships to AWS.
Start a certification
Test your knowledge
Test your AWS knowledge
9 questions · ~5 min
A broad readiness check across compute, storage, networking, security, databases, and resilience.
Start quiz →Test your AI/ML readiness
10 questions · ~5 min
Core AI/ML concepts — supervised vs. unsupervised, overfitting, model drift, bias and fairness, and generative AI.
Start quiz →--model-id anthropic.claude-3-haiku-20240307-v1:0 \
--body '{"prompt": "Build something real."}'
Real articles, not a sales page
Practitioner-level AI/ML writing — no fluff, no fabricated claims.
GenAI Engineer Interview Questions, With Sample Answers
What GenAI and LLM engineer interviews actually ask, grouped by topic, with what a strong answer sounds like and what a weak one gives away.
October 13, 2026
Fine-tuning versus continued pre-training for Amazon Nova 2
Imagine an interviewer asks how to adapt Amazon Nova 2 Lite for a specialist domain: should you feed it a vast library of raw documents, or show it carefully…
October 10, 2026
The AI/ML Lifecycle and What MLOps Adds, for AWS AIF-C01
Each lifecycle stage by what it produces, the two feedback loops around deployment, which AWS service fits each stage, and the seven things MLOps adds.
October 8, 2026
Where are you headed?
Pick the path that fits you. If you're not sure, we'll help you figure it out.
36 projects, all open source on GitHub ↗
AI Engineering Fundamentals
You're newer to engineering and want to get into AI/ML. You don't need ML math — you need to build and deploy real things.
12 projects · Starts from Python
Outcome: junior AI engineer–ready
First project: Document Chat — A CLI tool that loads a PDF or text file and answers questions about it, with streaming output and exact source citations for every answer.
See every project in AI Engineering Fundamentals →AI-Augmented Engineering
You have 2–8 years of software experience. AI is everywhere and you feel behind. These projects put AI into the work you already know how to do.
12 projects · Any language welcome
Outcome: AI in your toolkit
First project: Code Review Bot — A CLI that reads a git diff, chunks it per file, and uses an LLM in JSON mode to return prioritized, structured findings by severity and category.
See every project in AI-Augmented Engineering →ML Engineering on AWS
You've done some ML or data science and want to own the full system — training, serving, monitoring — in production on AWS.
12 projects · Python + ML exposure
Outcome: own ML systems end-to-end
First project: Local to SageMaker — Trains an XGBoost classifier on the UCI Adult dataset locally with a CLI script, then submits the identical script as a managed SageMaker training job via a least-privilege CDK-provisioned IAM role, with a cost estimator and CloudWatch metric fetcher.
See every project in ML Engineering on AWS →Build Your Own Path
None of the above quite fit? Answer 4–5 questions about your situation and we'll assemble a custom path from our project library and email it to you.
Takes 2 minutes · Reply in 48h
Not sure which path fits you? Answer 3 questions.
Q1: How long have you been writing code?
Q2: What's your goal right now?
Q3: What have you already tried?
How it works
Pick your path
You tell us where you are and where you're headed. We give you the exact sequence.
Get projects delivered
Every fortnight, one project lands in your inbox: what to build, how to build it, what you'll learn.
Build and ship
Each project has a GitHub repo you fork, deploy to AWS, and own. Add it to your portfolio today.
Stuck? Reply to any email and the mentor responds personally.