ENJYRA FREE V7
Curriculum
9 core lessons build one local AI assistant from workspace to Docker. Complete 9/9 to finish the course. AWS, Azure, and GCP are separate optional bonus labs.
CORE COURSE · 9 LESSONS
Build one working local AI system
Build Your AI Engineering Workspace
Create the project workspace and verify VS Code, Python, Git, and Docker before building the AI system.
Run Your First Local AI Model
Run Ollama in Docker, verify its local API, and generate your first response with llama3.2:3b.
Call AI from Python
Send a Python HTTP request to local Ollama, inspect the JSON response, and recover from a stopped runtime.
Build Your First AI Application
Turn the model request into an interactive command-line assistant with input, output, and useful error handling.
Make AI Return Structured Data
Request JSON from the model, parse it safely, validate required fields, and handle malformed output.
Give AI Your Own Documents
Load your own text files, split them into searchable chunks, and retrieve relevant context without a vector database.
Build a Tiny RAG Application
Retrieve document context, ask Ollama for a grounded answer, cite the source, and refuse unsupported questions.
Dockerise and Debug Your AI App
Package the AI application in Docker, inspect logs from a real broken container, fix the command, and rebuild.
ENJYRA Free Capstone
Verify the complete local AI assistant end to end: documents, retrieval, Ollama, Docker, failure recovery, and cleanup.
OPTIONAL · 3 CLOUD LABS
Extend the engineering workflow to the cloud
AWS · Your First S3 Lab
Use the AWS CLI environment for a safe storage lifecycle with explicit cleanup.
Azure · Your First Blob Storage Lab
Use the Azure CLI environment for a safe storage lifecycle with explicit cleanup.
GCP · Your First Cloud Storage Lab
Use the Google Cloud CLI environment for a safe storage lifecycle with explicit cleanup.
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