AI+ Manufacturing Practitioner™

Course code: NPAP5501

Master AI-Driven Manufacturing Excellence for Smarter, Safer, and More Efficient Operations

The AI+ Manufacturing Practitioner certification prepares you to apply AI across production, maintenance, quality, supply chain, and plant operations. You will learn to improve efficiency, predict equipment failures, reduce downtime, strengthen quality control, and support faster operational decisions. The certification covers manufacturing data readiness, vision-based inspection, equipment monitoring, process optimization, AI architecture, implementation planning, responsible AI, security, and ROI measurement. You will also gain practical experience using tools such as ChatGPT, Teachable Machine, Looker Studio, Google Sheets, Miro, and draw.io.

75 EUR

91 EUR including VAT

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Course dates

Starting date: Upon request

Guaranteed

Type: Self-paced

Course duration: 8 hours

Language: en

Price without VAT: 285 EUR

Starting date: Upon request

Guaranteed

Type: Self-paced

Course duration: 8 hours

Language: en

Price without VAT: 75 EUR

	Starting date	Type	Course duration	Language	Price without VAT
G	Upon request	Self-paced	8 hours	en	285 EUR	Register

G	Upon request	Self-paced	8 hours	en	75 EUR	Register
G	Guaranteed course

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Course description

AI Foundations and Opportunities

Understand AI fundamentals, plant-level applications, human–AI collaboration, business value, practical use cases, and hands-on manufacturing scenarios.

Core Manufacturing AI Applications

Explore vision-based inspection, predictive maintenance, equipment reliability, operational analytics, production planning, intelligent automation, and practical exercises.

Manufacturing Data Readiness

Learn about manufacturing data types, quality requirements, readiness challenges, real-world use cases, and KPI dashboard creation using Looker Studio.

Industrial AI Systems and Architecture

Compare deployment approaches, understand AI system structures, evaluate integration options, and map industrial AI architectures using Miro or draw.io.

AI Implementation and Scaling

Identify valuable AI opportunities, design pilots, measure impact, address implementation constraints, and create scalable adoption roadmaps.

Responsible AI, Safety, and Security

Apply data governance, cybersecurity, operational safety, human oversight, escalation controls, and AI risk assessment practices.

AI Performance and Business Value

Examine project success and failure factors, apply ROI frameworks, compare industry adoption, and estimate measurable operational benefits.

Future Manufacturing Technologies

Explore digital twins, intelligent monitoring, generative AI, emerging technologies, adoption trends, and phased AI roadmap planning.

End-to-End Manufacturing Project

Define a manufacturing problem, assess readiness, select an AI use case, evaluate solutions, develop a roadmap, and communicate business value.

Course structure

Module 1: AI in Manufacturing - Context and Opportunities

1.1 AI Fundamentals in Manufacturing
1.2 AI Across Plant Operations
1.3 Human and Business Context of AI Adoption
1.4 Use-Cases
1.5 Case Studies
1.6 Hands-On

Module 2: Core AI Applications in Manufacturing

2.1 Vision AI in Manufacturing
2.2 Maintenance and Reliability AI
2.3 Operational AI in Manufacturing
2.4 AI in Planning and Automation
2.5 Use-Cases
2.6 Case Studies
2.7 Hands-On Exercise

Module 3: Manufacturing Data and Readiness

3.1 Types of Manufacturing Data
3.2 Data Readiness Requirements
3.3 Common Readiness Challenges
3.4 Use-Cases
3.5 Case Studies
3.6 Hands-On Exercise: Manufacturing KPI Dashboard Creation using Looker Studio

Module 4: AI Systems and Architecture in Manufacturing

4.1 Deployment Approaches for Industrial AI
4.2 AI System Structure
4.3 Integration and Solution Evaluation
4.4 Use-Cases
4.5 Case Studies
4.6 Hands-On Exercise: AI System Architecture Mapping Exercise using Miro or draw.io

Module 5: Implementing AI in Manufacturing

5.1 Identifying and Prioritizing AI Opportunities
5.2 Pilot and Proof-of-Concept Design
5.3 Measuring and Scaling AI Impact
5.4 Real-World Implementation Constraints
5.5 Use-Cases
5.6 Case Studies
5.7 Hands-On Exercise: AI Pilot and Implementation Roadmap Workshop using Miro

Module 6: Responsible AI, Safety, and Security

6.1 Responsible AI in Industrial Operations
6.2 Governance and Data Responsibility
6.3 Security and Safety Risks
6.4 Human Oversight and Escalation
6.5 Use-Cases
6.6 Case Studies
6.7 Hands-On Exercise: AI Risk and Governance Checklist Exercise using Google Sheets