·DevOps
Module 14 Lesson 1: Dockerizing a Node.js Full-Stack App
From code to container. A step-by-step guide to containerizing a modern Node.js application with a React frontend, Express backend, and MongoDB database.
hands-on
67 articles
·DevOps
Module 5 Lesson 2: Your First docker-compose.yml
Hands-on with YAML. Learn the structure of a Compose file and build a working stack with a web server and a database in minutes.
·DevOps
Module 5 Lesson 5: Real-World Multi-Container Example
The capstone of the basics. Build a complete Voting App architecture with a Python worker, a Redis queue, a Postgres DB, and a Node.js results page.
·DevOps
Module 3 Lesson 1: Hello World Pipeline
Your first automated build. Follow a step-by-step guide to creating, committing, and running your very first GitLab CI/CD pipeline.
·AWS Bedrock
Module 10 Wrap-up: Designing your Agent
Hands-on: Design the mission statement and tool-set for your first Bedrock Agent.
·AWS Bedrock
Module 11 Wrap-up: Giving your Agent Skills
Hands-on: Write a Lambda function and a schema for an agent that can track package deliveries.
·AWS Bedrock
Module 12 Wrap-up: The Master Planner
Hands-on: Trace the execution of a multi-step agent and identify reasoning bottlenecks.
·AWS Bedrock
Module 13 Wrap-up: The Team Lead
Hands-on: Design the architecture for a multi-agent system that handles a complex business workflow.
·AWS Bedrock
Module 14 Wrap-up: The Collaborative AI
Hands-on: Design a human-in-the-loop workflow for a high-value financial transaction agent.
·AWS Bedrock
Module 15 Wrap-up: The Observability Suite
Hands-on: Create a CloudWatch dashboard that tracks your agent's success rate and token spend.
·AWS Bedrock
Module 16 Wrap-up: The Security Audit
Hands-on: Design a secure architecture that involves IAM, Secrets Manager, and Guardrails.
·AWS Bedrock
Module 17 Wrap-up: Ready for Production
Hands-on: Design a deployment strategy for a mission-critical AI agent.
·AWS Bedrock
Module 18 Wrap-up: The Enterprise Mindset
Hands-on: Identify a business workflow that requires the control and state management of AgentCore.
·AWS Bedrock
Module 19 Wrap-up: Mapping the Logic
Hands-on: Design a multi-node AgentCore graph that includes AI reasoning and data validation.
·AWS Bedrock
Module 2 Wrap-up: Getting Ready for Code
Hands-on: Verify your AWS configuration and enable your first foundation models.
·AWS Bedrock
Module 20 Wrap-up: The High-Fidelity Assistant
Hands-on: Design an AgentCore workflow that uses a critic node to verify RAG output.
·AWS Bedrock
Module 3 Wrap-up: Your First AI Call
Hands-on: Write a script that compares the outputs of two different models using the same prompt.
·AWS Bedrock
Module 4 Wrap-up: Engineering the Instruction
Hands-on: Robust prompt engineering to reduce hallucinations and maximize cost-efficiency.
·AWS Bedrock
Module 5 Wrap-up: Real-time Performance
Hands-on: Build a streaming CLI chat application that handles tokens as they arrive.
·AWS Bedrock
Module 6 Wrap-up: Shipping the API
Hands-on: Build a functional REST API with FastAPI that exposes multiple Bedrock models.
·AWS Bedrock
Module 7 Wrap-up: Designing the Library
Hands-on: Design your first Knowledge Base and select your chunking strategy.
·AWS Bedrock
Module 8 Wrap-up: Real-World RAG
Hands-on: Build a Python script that answers questions about your S3 documents and prints the citations.
·AWS Bedrock
Module 9 Wrap-up: The Safe Assistant
Hands-on: Implement a Bedrock Guardrail and verify your grounding instructions.
·Generative AI
Module 3 Wrap-up: Becoming a Prompt Architect
Reviewing professional prompting techniques and completing a structured output project.
·Generative AI
Module 4 Wrap-up: The Multimodal Creator
Reviewing non-text GenAI and practicing image generation and code refactoring.
·Generative AI
Module 5 Wrap-up: Building with Intelligence
Reviewing the AI tech stack and building a simple RAG pipeline and local AI setup.
·LangChain
Module 10 Wrap-up: Building Your First Agent
Hands-on: Combine tools, memory, and reasoning into a single autonomous Research Agent.
·LangChain
Module 11 Wrap-up: Getting Reliable Data
Hands-on: Build an Information Extraction agent that converts raw text into a clean Python object.
·LangChain
Module 12 Wrap-up: Inspecting the Machine
Hands-on: Build a Cost-Monitoring Callback that calculates and prints the price of every AI request.
·LangChain
Module 13 Wrap-up: Professional Observability
Hands-on: Set up LangSmith and trace a complex multi-tool agentic decision.
·LangChain
Module 14 Wrap-up: Shipping to Millions
Hands-on: Create a production-ready FastAPI endpoint with caching and retry logic.
·LangChain
Module 15 Wrap-up: Your App on the Internet
Hands-on: Finalize your deployment package and learn about cloud hosting options for LangChain apps.
·LangChain
Module 3 Wrap-up: Your Reusable Library
Hands-on: Build a reusable prompt library and implement a few-shot dynamic classifier.
·LangChain
Module 4 Wrap-up: Designing Complex Flows
Hands-on: Build a Parallel-Sequential Research Chain that writes, reviews, and translates a report.
·LangChain
Module 5 Wrap-up: Processing Your Knowledge Base
Hands-on: Build a pipeline that loads a multi-page PDF and splits it into optimized chunks.
·LangChain
Module 6 Wrap-up: Storing Your AI's Memory
Hands-on: Build a local knowledge base using ChromaDB and perform semantic queries.
·LangChain
Module 7 Wrap-up: Building a Q&A Bot
Hands-on: Finalize your first production-ready RAG system over your own local documents.
·LangChain
Module 8 Wrap-up: Conversations that Stick
Hands-on: Build a persistent chatbot that remembers your name across different CLI sessions.
·LangChain
Module 9 Wrap-up: Giving Your Agent Powers
Hands-on: Build a toolbox for an agent that can multiply numbers and search Wikipedia.
·Agentic AI
Module 1 Wrap-up: Your First Agentic Decision
Hands-on: Compare a chatbot vs an agent workflow and identify agent-worthy problems.
·Agentic AI
Module 10 Wrap-up: Scaling with Governance
Hands-on: Design a configuration-driven agent with a global PII-filtering policy.
·Agentic AI
Module 11 Wrap-up: The Strategic Selection
Hands-on: Use a decision matrix to select the right framework for three real-world business scenarios.
·Agentic AI
Module 12 Wrap-up: Engineering for 99% Accuracy
Hands-on: Build a self-correcting agent loop that uses Pydantic to validate outputs.
·Agentic AI
Module 13 Wrap-up: The Secure Perimeter
Hands-on: Secure an agent against jailbreaking and implement a PII redaction layer.
·Agentic AI
Module 14 Wrap-up: The Sovereign Architect
Hands-on: Build a state-machine governed agent that handles a login and payment flow.
·Agentic AI
Module 15 Wrap-up: Engineering for the Millions
Hands-on: Build a simple RAG agent that retrieves context from a local Vector DB before answering.
·Agentic AI
Module 16 Wrap-up: The Experience Engineer
Hands-on: Design a dashboard concept for a multi-agent research crew that shows planning and status.
·Agentic AI
Module 2 Wrap-up: Your First Minimal Agent
Hands-on: Build a manual, single-step agent loop from scratch in Python.
·Agentic AI
Module 3 Wrap-up: The Strategic Architect
Hands-on: Design a planner-executor flow for a multi-step research task.
·Agentic AI
Module 4 Wrap-up: Your First LangChain Agent
Hands-on: Build a tool-using LangChain agent and observe the reasoning traces in real-time.
·Agentic AI
Module 6 Wrap-up: Migrating to the Graph
Hands-on: Convert a simple LangChain AgentExecutor into a controlled LangGraph workflow.
·Agentic AI
Module 7 Wrap-up: The Master of Graphs
Hands-on: Build a complex research agent with routing, loop guards, and a validation gate.
·Agentic AI
Module 8 Wrap-up: Your First AI Crew
Hands-on: Build a two-agent research crew that identifies tech trends and writes a report.
·Agentic AI
Module 9 Wrap-up: Engineering for Real-Time
Hands-on: Build an event-driven agent pipeline that detects toxicity and triggers a response.
·AI & LLMs
Module 1 Wrap-up: Inspecting Your Resources
Prepare your machine for Ollama. A hands-on guide to checking your hardware and selecting your first model.
·AI & LLMs
Module 10 Wrap-up: Building Your Local Q&A System
Hands-on: The complete RAG project. Index a folder of text files and build a bot that can answer questions about them.
·AI & LLMs
Module 12 Wrap-up: Hardening Your Local AI
Hands-on: Secure your environment. Final checks for a professional, compliant local AI setup.
·AI & LLMs
Module 13 Wrap-up: Your High-Performance Stack
Hands-on: Deployment with Docker Compose. Building a multi-container stack with Ollama and a Web UI.
·AI & LLMs
Module 14 Wrap-up: The Full-Scale Transition
Hands-on: Deploying to a remote server. Final operational checks before going live.
·AI & LLMs
Module 2 Wrap-up: Your First Local Chat
Hands-on session: Pulling your first model and having a high-speed conversation with a local AI.
·AI & LLMs
Module 3 Wrap-up: The Model Comparison Challenge
Put your knowledge to the test. Compare Llama, Mistral, and Gemma on speed, humor, and logic.
·AI & LLMs
Module 4 Wrap-up: Measuring Model Efficiency
Hands-on: Benchmarking your machine. Compare quantization levels and measure memory usage in real-time.
·AI & LLMs
Module 5 Wrap-up: Engineering Your Custom Bot
Hands-on: Creating a specialized AI persona from scratch. Move beyond the default registry.
·AI & LLMs
Module 6 Wrap-up: Bringing Hugging Face Home
Hands-on: The full workflow from Hugging Face download to Ollama creation.
·AI & LLMs
Module 7 Wrap-up: The Optimization Challenge
Hands-on: Benchmarking your machine. Compare quantization levels and measure memory usage in real-time.
·AI & LLMs
Module 8 Wrap-up: Building Your Python Chatbot
Hands-on: Creating a fully functional, streaming terminal chatbot using Python and Ollama.
·AI & LLMs
Module 9 Wrap-up: Creating the Structured Expert
Hands-on: Combine system prompts, JSON mode, and negative constraints to build a production-ready data extractor.