AI Fundamentals for Production
Master the mechanics of industrial pattern recognition and the core logic behind data-driven anomaly detection on the shop floor.
Five modules empower you to demystify "Black Box" algorithms, understand how AI flags deviations in machine behavior, and secure your role as an informed collaborator in the transition to predictive maintenance.
Start Dates
17 March 2026
17 April 2026
12 May 2026
Level
Beginner
Duration
8 hours total
2 weeks training (online/classroom Vaduz)
1 full day (on-site)
Language
English or German (customer's choise)
Course Fee
CHF 850 (online/classroom Vaduz)
CHF 1195 (on-site)
(including all materials and certificate)
Certificate
Industrial AI Technician (Level I)
Course Outline
This foundational workshop resolves the "Black Box" anxiety surrounding machine learning by explaining how data transitions into actionable insight.
The curriculum focuses on the practical mechanics of model training for industrial environments, enabling operators and technicians to identify pattern deviations, such as bearing failures or heat spikes, before they escalate into production downtime.
The Problem Solved
Demystifying the Black Box: We resolve the uncertainty of autonomous alerts. We provide the fundamental technical logic required for maintenance and production staff to interpret AI-driven pattern recognition and anomaly detection with absolute confidence.
Key Skills & Competencies
1. Industrial Data Literacy: Differentiating between operational "noise" and critical "signals."
2. Anomaly Detection Basics: Recognizing how AI flags deviations in vibration or heat.
3. Predictive Maintenance Logic: Mastering the shift from reactive to proactive upkeep.
Course Modules
1. The Industrial AI Engine: How ML models "think." Understanding the logic of the shop floor.
2. Pattern Recognition: Spotting real-time defects. Identifying deviations in live data streams.
3. Basic Data Labeling: Defining "Normal." Teaching the AI to recognize healthy machine states.
4. OEE Insights: AI-driven effectiveness. Calculating true equipment performance in real-time.
5. Introduction to AI Tools: Hands-on dashboards. Navigating predictive maintenance interfaces.
Learning objectives and format
By the end of the course, you will be able to master the fundamentals of predictive maintenance logic and the labeling of industrial datasets.
You’ll navigate five modules covering machine learning engines, pattern recognition, and OEE dashboards, transforming from a passive observer of technology into an informed technical collaborator.
Key Objectives
- Define how machine learning models process vibration, heat, and sound data
- Distinguish between operational data "noise" and actionable failure "signals"
- Perform basic data labeling to train AI on "Normal" vs. "Anomaly" states
- Interpret predictive maintenance dashboards to forecast component end-of-life
- Apply AI-driven OEE metrics to identify specific production bottlenecks
- Communicate technical anomalies effectively to Engineering and Data teams
Learning Format
- Analyze real-world sensor logs from industrial bearings and motor assemblies
- Visualize the "Pattern Recognition" path from raw data to maintenance alerts
- Identify specific anomaly types within simulated production environments
- Draft "Normal State" labels for common shop-floor machinery
- Test predictive dashboard accuracy against known historical failure data
- Complete the final assessment to earn your Industrial AI Technician certificate
Who is this course for?
Prerequisites
Basic familiarity with shop-floor machinery. Completion of AI Literacy for Industrial Workers is recommended.
Target Audience
Machine Operators, Junior Engineers, and Maintenance Technicians looking to specialize in Industry 4.0.
