Beginner
Half day (09:00–12:30)
n8n Fundamentals: Automate Your Operations
Build real automations in a morning. Triggers, nodes, credentials and error handling — using your own tools, not toy examples.
- Build and deploy a working automation end to end
- Connect n8n to APIs, databases and webhooks with stored credentials
- Handle failure properly — retries, error branches and alerting
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Intermediate
Half day (13:30–17:00)
LLMs Beyond the Chat Window
Stop pasting into a chat box. Call models from code, get structured output you can rely on, and control cost and latency.
- Call an LLM API directly and handle its failure modes
- Get reliable structured output instead of prose you have to parse
- Use function/tool calling to let a model trigger real actions
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Intermediate
Full day (09:00–17:00)
Building AI Agents in n8n
Agents that use tools, remember context and take multi-step action — built visually in n8n and running by the end of the day.
- Build an agent that selects and calls tools to complete a task
- Add memory so an agent holds context across a conversation
- Ground an agent on your own documents with a vector store
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Advanced
Full day (09:00–17:00)
Building Production AI Agents
The code-level day: orchestration, guardrails, human-in-the-loop, evaluation and observability. What separates a demo from a system.
- Design an agent architecture that fails safely under load
- Implement approval gates and human-in-the-loop for consequential actions
- Build an evaluation set so you can tell whether a change helped
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Advanced
Full day (09:00–17:00)
Runs every month
AI Agents for Industrial Operations
Agents that read live telemetry, reason about asset condition and raise work orders — with the guardrails that safety-critical environments demand.
- Connect an agent to live time-series telemetry and query it reliably
- Ground answers on manuals, P&IDs and maintenance history with retrieval
- Turn a prediction into an action — raising a work order automatically
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Intermediate
Full day (09:00–17:00)
Applied AI for Engineers
Practical machine learning for engineering data — anomaly detection, forecasting and edge deployment, on real sensor readings.
- Frame an engineering problem as a tractable ML problem
- Build anomaly detection that survives contact with noisy real data
- Produce a short-horizon forecast and know how far to trust it
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