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Learning Paths Across the Four Zero to Hero Sites

In one line: Seven short routes that cross cloud, data, security and AI, each a handful of pages in a sensible order, with a reason for every step and the certifications that match. Last reviewed: 2026-11-07

How to use these paths

Real jobs do not stay inside one subject. A cloud engineer ends up owning IAM policy. A data engineer is asked to feed a model. A security engineer is handed an LLM agent to review. Each path below starts from a job you might have today and walks through pages on whichever site teaches the next skill.

  • Every step is one existing page. Read it, try what it describes, then move on. You do not need to finish a whole site first.
  • Each step says why it sits where it does, so you can skip one you already know.
  • The certifications at the end are optional. They are the exams that line up with the path, not a required finish line.
  • Not sure these are the right sites for you? Which site should I start with? compares all four.
Path You start as You finish able to
1. Zero to first cloud job Someone new to tech Build and explain a small cloud system
2. Cloud engineer who needs security A cloud or DevOps engineer Lock down accounts, identities and secrets
3. Data engineer moving into ML A data engineer Prepare features and ship a model
4. Security engineer for AI systems A security engineer Threat model and test an LLM application
5. Analyst to analytics engineer A spreadsheet or BI analyst Own a tested, modeled warehouse layer
6. AI engineer who deploys A developer building AI apps Run an AI app in production
7. Cloud engineer who runs data platforms A cloud engineer Stand up and govern a lakehouse

1. Zero to first cloud job

For someone who has never opened a terminal and wants a junior cloud role.

  1. Terminal basics (Cloud) - every cloud task eventually happens in a shell, so this comes first.
  2. What is a server? (Cloud) - cloud is rented servers, so you need a clear picture of one before the rest makes sense.
  3. What is cloud computing? (Cloud) - the vocabulary: regions, services, pay-as-you-go.
  4. Cloud from Scratch (Cloud) - the full path through compute, storage, networking, identity and containers.
  5. IAM explained (Security) - identity is the first thing every cloud team asks a new hire about, and Security teaches it in depth.
  6. Deploy a 3-tier app (Cloud) - a build you can show an interviewer.
  7. Cloud engineer interview prep (Cloud) - the questions to practice once you have built something.

Matching certifications: AWS Cloud Practitioner, then AWS Solutions Architect Associate or Azure AZ-104. The cloud engineer roadmap puts them in order.

2. Cloud engineer who needs security

For someone who already deploys to the cloud and is now answerable for how safely.

  1. Shared responsibility model (Security) - what the provider secures and what is left to you.
  2. IAM explained (Security) - users, roles and policies, the root of most cloud breaches.
  3. Least privilege (Security) - how to shrink permissions without breaking deployments.
  4. Secrets management (Security) - where keys and tokens live once they are out of your code.
  5. Multi-account and landing zones (Cloud) - the account structure that makes guardrails enforceable.
  6. CSPM and CNAPP explained (Security) - tools that find misconfigurations across your accounts.
  7. Least-privilege IAM review (Security) - audit a real account and fix what you find.
  8. Cloud security engineer roadmap (Security) - the career route this path opens.

Matching certifications: CompTIA Security+, AWS Security Specialty (SCS-C03) and CCSK.

3. Data engineer moving into ML

For someone who builds pipelines and now wants to train, serve and monitor models.

  1. Data pipelines and orchestration (Data) - the skill you already have, restated as the base ML pipelines are built on.
  2. What is machine learning? (AI) - the vocabulary of features, labels and models.
  3. Training, validation and overfitting (AI) - why data splits matter and how leakage ruins results.
  4. Feature engineering and data prep (AI) - where a data engineer's strengths count most.
  5. Feature stores for data engineers (Data) - serving the same features in training and in production.
  6. Tabular ML end to end (AI) - one full build from raw table to evaluated model.
  7. Deploy an ML model (AI) - getting it behind an endpoint.
  8. ML Engineer Path (AI) - the longer road once the first model ships.

Matching certifications: AWS Machine Learning Engineer Associate, Google Professional Machine Learning Engineer and Databricks ML Associate.

4. Security engineer for AI systems

For someone who knows threat modeling and now has to review an LLM feature or agent.

  1. LLM basics (AI) - you cannot threat model what you cannot describe: tokens, context, tools.
  2. Threat modeling explained (Security) - the method you will reuse in step 4.
  3. Prompt injection explained (AI) - the attack with no equivalent in classic application security.
  4. AI threat modeling (AI) - applying the method to models, data and tools.
  5. OWASP LLM Top 10 (AI) - the checklist reviewers expect to see.
  6. Agent security (AI) - what changes when the model can act.
  7. LLM red teaming (AI) - how to test rather than guess.
  8. Secrets management (Security) - agents hold credentials, so apply the classic controls to them.

Matching certifications: CompTIA Security+ as the base, then IAPP AIGP, ISACA AAISM and CSA TAISE.

5. Analyst to analytics engineer

For someone fluent in spreadsheets or BI tools who wants to own the modeled layer under the dashboards.

  1. Spreadsheets to SQL (Data) - the bridge from the tool you know.
  2. SQL joins explained (Data) - the most common source of wrong numbers.
  3. Window functions (Data) - running totals, ranks and comparisons without self-joins.
  4. Data modeling for analytics (Data) - facts, dimensions and star schemas.
  5. Analytics engineering and dbt (Data) - models, tests and docs as code.
  6. Build a dbt project (Data) - practice on a real project.
  7. Git basics (Cloud) - analytics engineering runs on pull requests and review.
  8. Analytics engineer roadmap (Data) - where to go after the first dbt project.

Matching certifications: Microsoft PL-300, Databricks Data Analyst Associate and SnowPro Core.

6. AI engineer who deploys

For a developer who has a working prototype and now has to run it for real users.

  1. AI Engineer Path (AI) - the map of what you already know and what is missing.
  2. Containers vs VMs (Cloud) - how the app gets packaged.
  3. CI/CD explained (Cloud) - ship changes without surprises.
  4. Infrastructure as code (Cloud) - make the environment reproducible.
  5. Secrets management (Security) - API keys for model providers are valuable; keep them out of images and repos.
  6. LLMOps and observability (AI) - traces, cost and quality signals in production.
  7. Evals for LLMs (AI) - the regression tests for a non-deterministic system.
  8. Guardrails architecture (AI) - the controls that sit around the model.

Matching certifications: Azure AI-102, HashiCorp Terraform Associate and AWS Developer Associate.

7. Cloud engineer who runs data platforms

For someone who provisions infrastructure and is now asked to host the warehouse or lakehouse.

  1. Managed databases explained (Cloud) - what the provider runs for you and what stays yours.
  2. OLTP vs OLAP (Data) - why the application database and the analytics store differ.
  3. Warehouses, lakes and lakehouses (Data) - the three shapes of analytics storage.
  4. ETL vs ELT (Data) - where transformation runs and what that does to cost.
  5. Medallion architecture (Data) - the bronze, silver and gold layout most platforms use.
  6. Encryption explained (Security) - keys and encryption at rest for the data you now hold.
  7. Warehouse cost control (Data) - the bill is the first thing your team will ask about.
  8. Backup and point-in-time restore (Data) - prove you can recover before you need to.

Matching certifications: AWS Data Engineer Associate, Google Professional Data Engineer and Azure DP-900.

Next steps