📚 E-Courses

Full course guides, read right here

Every guide is a complete written course, not a summary. The reasoning first, then runnable code, a diagram where the shape is the hard part, and the gotchas that only turn up once something has broken in production.

Reading is only the first third

Read it

Contents, search and your own highlights. Picks up where you left off.

Evaluation

25 questions from that guide, two minutes each, with a breakdown.

Mock Interview

Spoken, on the same material. Feedback after every answer.

All three are included with Skiiling Pro, which also unlocks Role Prep and Practical Labs and Skill Plans.

32 guides3,080 pages914 topics800 evaluation questions
☁️ProCloud Computing

AWS Cloud Engineer

Global InfrastructureVPC DesignAWS Shield & WAF

Core AWS services, networking, compute, storage, databases, infrastructure as code and production operations, with runnable examples throughout.

114
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6
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30
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🌐ProCloud Computing

Google Cloud Platform Engineer

GCP Global InfrastructureVPC & Shared VPCCloud Build & Artifact Registry

Building and operating on GCP: projects and IAM, compute, storage, BigQuery, Kubernetes and data pipelines, with runnable examples throughout.

114
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6
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30
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🔷ProCloud Computing

Microsoft Azure Cloud Engineer

Azure Regions & Availability ZonesVirtual Networks & PeeringMicrosoft Defender for Cloud

Azure from the ground up: subscriptions and Entra ID, virtual machines, networking, storage, App Service, AKS and Bicep, with runnable examples throughout.

117
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6
Modules
30
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🗄️ProBig Data

Big Data with Hadoop and Spark

What is Big Data?Structured Streaming BasicsRunning Spark on EMR / Dataproc

Distributed storage and processing: HDFS, YARN, MapReduce, Spark core, Spark SQL, streaming and the Hive and Kafka ecosystem, with runnable examples throughout.

94
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6
Modules
30
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✨ProArtificial Intelligence

Generative AI

What is Artificial Intelligence?LangChain ArchitectureModel Serving (vLLM, TGI)

From how transformers generate text to prompting, embeddings, retrieval augmented generation, fine tuning and evaluation, with runnable examples throughout.

100
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15
Modules
75
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🤖ProArtificial Intelligence

Agentic AI

What are AI Agents?Reliability & RetriesAgent Audit Trails & Compliance

Designing systems that plan, call tools, remember and act: agent loops, tool schemas, memory, multi-agent orchestration and production safeguards, with runnable examples throughout.

106
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15
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75
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🍃ProProgramming

Spring Cloud and Microservices

Monolith vs Microservices Trade-OffsSpring Cloud Config Server & Git BackendDistributed Tracing with Micrometer & Zipkin

Service decomposition, discovery, gateways, configuration, resilience, messaging and observability with Spring Boot and Spring Cloud, with runnable examples throughout.

119
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5
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25
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🐍ProProgramming

Python Programming

Python Installation & SetupReading & Writing FilesGenerators & Iterators

Python from first principles to production: the core language, data structures, object orientation, files and errors, web services with Flask, and concurrency, testing and packaging, with runnable examples throughout.

109
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6
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31
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🧱ProData & AI Platforms

Databricks Data Intelligence Platform

Lakehouse Architecture & Workspace SetupSQL Warehouses & Query ExecutionVector Search & Embedding Pipelines

The whole Databricks platform in one guide: the lakehouse and Delta Lake, data engineering with Spark and declarative pipelines, SQL warehousing, Unity Catalog governance, machine learning with MLflow, and generative AI with Vector Search, agents and AI/BI Genie, with runnable examples throughout.

158
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7
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35
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🖼️ProGenerative AI

Multimodal RAG with Gemini

Why Text-Only RAG Fails on Rich DocumentsNatural Language Queries over Financial DocumentsContainerising the Pipeline with Cloud Run

Standard RAG only reads text. Modern businesses need to query charts, tables, and videos. Build a pipeline that uses Gemini 1.5 Pro to extract and index insights from a library of financial PDF reports containing complex graphs.

76
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4
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20
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🔁ProAgentic AI

The Agent Loop

Chatbot vs Agent: Why Planning Changes EverythingExecuting a Plan Step with Tool CallsDetecting Failure: Heuristics vs LLM-as-Judge

Unlike simple chatbots, Agentic AI can think about its mistakes. Implement a Python-based agent loop where an LLM creates a plan, executes a step, checks the result, and pivots if the outcome is wrong.

52
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3
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15
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🛡️ProAgentic AI

Guardrails with NeMo and LlamaGuard

Real-World AI Failures from Missing GuardrailsLlamaGuard: Intent Classification CategoriesOutput Validation: Fact-Checking, PII Detection, Toxicity

Companies are terrified of their AI going off-script. Build a pre-processing layer that filters user intent and a post-processing layer that validates AI output before the user sees it.

54
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3
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15
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🕸️ProAgentic AI

Multi-Agent Orchestration

Why One Agent Is Not Enough for Complex TasksDefining the Writer Agent and Its TaskSequential vs Hierarchical Agent Execution

Complex tasks require different roles — a Coder agent, a Reviewer agent, a Researcher agent. Set up a Creative Agency multi-agent system where one agent writes content and another critiques it until a quality score is met.

49
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3
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15
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🪨ProGenerative AI

AWS Bedrock Knowledge Bases

Amazon Bedrock: Models, APIs, and Managed ServicesCreating an S3 Data Source and Uploading DocsS3 Event Notification → Lambda → Bedrock Sync Trigger

Bedrock is the fastest way for AWS users to deploy RAG without managing infrastructure. Connect an S3 bucket to Amazon Bedrock to create a Living Documentation bot that updates every time a new doc is uploaded.

58
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3
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🔎ProGenerative AI

Vertex AI Search and Conversation

Vertex AI Search vs Building Your Own RAGImporting a Product Catalog into a Structured Data StoreVertex AI Conversation: Multi-Turn Chat on Your Data

Google's no-code RAG solution. Deploy a production-ready search engine for an e-commerce site using Google's pre-indexed retail models in under 30 minutes.

59
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3
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15
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🦜ProGenerative AI

LangChain, LangGraph and LangSmith

LangChain architecture and core abstractionsWhy LangGraph: limitations of linear chainsLangServe: serving chains as FastAPI endpoints

Master the full LangChain ecosystem — build production chains, stateful agentic workflows with LangGraph, and debug and evaluate with LangSmith. One course, every tool.

113
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5
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36
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🧮ProData & AI Platforms

Vector Databases

What are embeddings and why they matterQdrant collections and vector configDecision matrix: latency, scale, cost, filtering needs

Learn every major vector database — ChromaDB for local prototyping, Pinecone for managed production, Qdrant for advanced filtering, plus Weaviate, Milvus, and pgvector. One course, all the options.

105
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6
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42
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📈ProLeadership

AI Strategy and GenAI Implementation

What LLMs actually do — capabilities without the mathBuild vs buy vs fine-tune vs API — the decision frameworkOperating model: centre of excellence vs federated teams

A non-code executive track for leaders who must decide where GenAI creates value — and where it burns budget. Build an AI strategy, evaluate build-vs-buy, quantify ROI, run a use-case portfolio, and stand up responsible-AI governance. Framed around board-level decisions, vendor evaluation, and change management, not model internals.

104
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5
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25
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🚨ProProduction Debugging

Debug Production: Generative AI

Diagnosing Tail Latency in Token StreamingStale and Out-of-Sync Vector IndexesTracing a Request Through the Whole Pipeline

The failures that only appear once a generative AI system carries real traffic: latency and cost blowouts, silent quality regressions, retrieval and context defects, and how to trace, log and run an incident on a system that is not deterministic.

66
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4
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20
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🏦ProProduction Debugging

Debug Production: Banking with Agentic AI

Double Execution on Tool RetryHallucinated Account Facts in Customer ConversationsCore Banking Latency and Agent Timeouts

What breaks when an autonomous agent touches a bank: double-executed payments, lost authorisation context, audit trails that cannot reconstruct a decision, and operating safely against core banking latency, cut-off windows and regulatory scrutiny.

83
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4
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20
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💳ProProduction Debugging

Debug Production: Fintech with Agentic AI

Duplicate Charges from Agent RetriesOpen Banking Connector BreakageTracing an Agent Decision End to End

Production failures in agent-driven fintech: duplicate charges and out-of-order webhooks, agents manipulated by untrusted merchant data, partner API drift, and the cost, compliance and observability work that keeps per-transaction margin intact.

86
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4
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20
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🩺ProProduction Debugging

Debug Production: Healthcare with Agentic AI

Hallucinated Clinical Facts and Their Blast RadiusEHR Integration Failures and Stale RecordsEvaluating Clinical Accuracy Without Causing Harm

Engineering and safety failures in clinical AI systems: hallucinated patient facts, protected health information leaking through logs and traces, EHR and terminology mapping defects, and the governance, evaluation and incident work that clinical deployment demands.

98
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4
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20
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🛒ProProduction Debugging

Debug Production: Retail with Agentic AI

Stale Inventory and OversellingPeak Traffic, Rate Limits and QueueingTracing an Order Decision End to End

Where agent-driven retail breaks: stale inventory and overselling, duplicate orders from retries, personalisation that leaks another customer's data, and running agents through peak season without wrecking conversion or margin.

91
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4
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20
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🚀ProBuild It End to End

Beginner to Hero: Generative AI Development

What You Are Actually Building and WhyWhat a Test Means When the Output Is Not DeterministicPackaging, Configuration and Environment Parity

Build a generative AI product from an empty directory to production: project setup, a development framework for prompts and retrieval, testing when the output is not deterministic, an evaluation framework you can run in CI, and deployment with cost, tracing and rollback.

105
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5
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25
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🛠️ProBuild It End to End

Beginner to Hero: Agentic AI Development

What Makes a System Agentic, and When Not to Build OneUnit-Testing Tools Without Calling the ModelRunning Long Tasks: Queues, Workers and Durability

Build an agent that does real work: the agent loop, tool design and permissions, testing trajectories without burning tokens, evaluating task success and cost, and deploying with budgets, approval paths and a kill switch.

93
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5
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25
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🟧ProBuild It End to End

Beginner to Hero: Agentic AI on AWS

The Managed Pieces and What They ReplaceTesting Cloud Code Without Deploying Every TimeInfrastructure as Code for the Whole Agent

Take an agent to production on AWS: identity and least privilege, managed knowledge bases, orchestration choices, contract testing against cloud services, evaluation in the pipeline, and infrastructure as code with cost controls and a runbook.

92
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5
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25
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🔵ProBuild It End to End

Beginner to Hero: Agentic AI on Google Cloud

The Managed Pieces and What They ReplaceTesting Cloud Code Without Deploying Every TimeInfrastructure as Code for the Whole Agent

Take an agent to production on Google Cloud: identity and least privilege, grounding and the vector index, orchestration choices, contract testing against cloud services, evaluation in the pipeline, and infrastructure as code with cost controls and a runbook.

100
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5
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25
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🧡ProBuild It End to End

Beginner to Hero: Agentic AI with Claude

What an Agent Is, and When Claude Is the Right Tool for ItTesting the Deterministic Majority Without Calling the ModelVersioning the Prompt, Model, Tools and Permissions as One Unit

Build a Claude agent that does real work: the tool-use loop, tool contracts and context management, testing trajectories without burning tokens, evaluating task success and cost, and deploying with budgets, tracing and a kill switch.

117
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5
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25
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⚙️ProBuild It End to End

Beginner to Hero: Agentic AI with OpenAI

What an Agent Is, and When a Single Model Call Is EnoughTesting the Deterministic Majority Without Calling the ModelVersioning the Prompt, Model, Tools and Permissions as One Unit

Build an OpenAI agent that does real work: the tool-calling loop, function schemas and structured outputs, testing trajectories without burning tokens, evaluating task success and cost, and deploying with budgets, tracing and a kill switch.

112
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5
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25
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🧭ProDevOps & Platform

DevOps Architecture

What a DevOps Architect Owns That an Engineer Does NotInfrastructure as Code at Organisation Scale: Modules, Layers and Blast RadiusSupply Chain Security: Provenance, SBOMs and Artefact Signing

Designing the path to production: platform architecture, pipeline topology, GitOps, SLOs and error budgets, supply chain security and the economics of delivery.

94
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6
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30
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🏛️ProData & AI Platforms

Data Architecture

What a Data Architect Owns That an Engineer Does NotBatch, Micro-Batch and Streaming Ingestion Trade-OffsReverse ETL and Operational Analytics

Dimensional modelling, key stability, conformed dimensions and data contracts, with the ingestion, quality, governance and cost decisions that decide whether a warehouse can be trusted.

122
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8
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40
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☁️ProCloud & Infrastructure

Cloud Architecture

What a Cloud Architect Owns That an Engineer Does NotNetwork Topology: Segments, Peering and TransitAccount and Subscription Topology as an Architecture Decision

Vendor-neutral cloud architecture: availability mathematics, multi-region tradeoffs, network and egress economics, identity boundaries, FinOps allocation and the landing zones that decide how an organisation scales.

120
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7
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35
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