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AI+ Manufacturing Practitioner™
USD$195.00 (GST excl.)
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.
Duration
Instructor-Led: 1 day (live or virtual)
Self-Paced: 8 hours of content
Self-Paced: 8 hours of content
Format
Online, self-paced
Exam
50 questions, 70% passing, 90 minutes, online proctored exam
Overview
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.
Prerequisites
Basic understanding of manufacturing operations such as production, maintenance, quality, and supply chain management. Learners should also be familiar with core AI, machine learning, and automation concepts, along with the ability to interpret operational data, dashboards, metrics, and trends. Awareness of digital systems such as MES, SCADA, ERP, sensors, and connected platforms is recommended, as well as basic business analysis skills to evaluate problems, feasibility, risks, value, and expected outcomes.
What you’ll learn
Manufacturing Professionals: Those who want to enhance their industrial skills by applying AI to improve production, maintenance, quality, planning, and operational decision-making.
Plant Managers and Manufacturing Leaders: Those who want to use AI-powered insights to optimize plant performance, reduce downtime, improve coordination, and make data-driven operational decisions.
Production and Process Engineers: Those who want to apply AI techniques for process monitoring, throughput improvement, bottleneck detection, scrap reduction, and production optimization.
Maintenance and Reliability Professionals: Those who want to use predictive maintenance, equipment health monitoring, failure prediction, and reliability analytics to improve asset availability and maintenance efficiency.
Quality Control and Inspection Teams: Those who manage product quality and want to use AI for defect detection, visual inspection, quality prediction, root-cause analysis, and process improvement.
Business Analysts and Data Professionals: Those who want to explore manufacturing analytics, sensor data, operational dashboards, demand forecasting, and data-supported decision-making.
Automation, IT, and OT Professionals: Those who want to understand how AI integrates with sensors, PLCs, MES, SCADA, ERP, cloud platforms, and industrial automation systems.
Manufacturing Transformation and Innovation Leaders: Those who want to implement AI strategies, evaluate use-cases, design pilots, measure ROI, and drive digital transformation across manufacturing environments.
Professionals Interested in AI-Powered Manufacturing: Those who want to build foundational expertise in combining artificial intelligence, industrial data, automation, and modern manufacturing practices.
Why this certification matters
- Builds job-relevant AI skills: Learn how AI supports production, maintenance, quality, supply chain, and plant operations.
- Reduces costly downtime: Apply predictive maintenance and equipment monitoring to identify issues earlier.
- Improves quality and efficiency: Use computer vision and analytics to detect defects, reduce waste, and optimize processes.
- Turns industrial data into action: Convert machine, sensor, maintenance, and production data into practical insights.
- Supports successful AI adoption: Evaluate use cases, design pilots, plan implementation, and scale solutions effectively.
- Demonstrates measurable value: Use manufacturing KPIs and ROI frameworks to track operational and business impact.
- Strengthens safety and trust: Apply responsible AI, cybersecurity, human oversight, and risk controls.
- Prepares you for smart manufacturing: Develop the capabilities needed to support connected, automated, and AI-enabled factories.
Tools you’ll use
Tableau
Qlik Sense
Lucidchart
PTC ThingWorx
GE Digital Proficy
Rockwell Automation FactoryTalk Analytics
Ignition by Inductive Automation
C3 AI
Uptake
Augury
Cognex VisionPro
UiPath
Sight Machine
Learning Modules
9 modules
1Module 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
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
2Module 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
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
3Module 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
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
4Module 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
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
5Module 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
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
6Module 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
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
7Module 7: AI Success, Failure, and ROI
7.1 AI Project Failures in Manufacturing
7.2 Success Patterns in AI Adoption
7.3 ROI Frameworks for Manufacturing AI
7.4 Industry Comparison
7.5 Use-Cases
7.6 Case Studies
7.7 Hands-On Exercise: AI ROI Estimation and Benefit Tracking
7.2 Success Patterns in AI Adoption
7.3 ROI Frameworks for Manufacturing AI
7.4 Industry Comparison
7.5 Use-Cases
7.6 Case Studies
7.7 Hands-On Exercise: AI ROI Estimation and Benefit Tracking
8Module 8: Future Trends in Manufacturing AI
8.1 Emerging AI Directions in Manufacturing
8.2 Digital Twins and Intelligent Monitoring
8.3 Generative AI in Manufacturing
8.4 Future Adoption Outlook
8.5 Use-Cases
8.6 Case Studies
8.7 Hands-On: AI Adoption Roadmap Creation
8.2 Digital Twins and Intelligent Monitoring
8.3 Generative AI in Manufacturing
8.4 Future Adoption Outlook
8.5 Use-Cases
8.6 Case Studies
8.7 Hands-On: AI Adoption Roadmap Creation
9Module 9: Capstone Project
9.1 Problem Definition and Scope
9.2 AI Use-Case Selection and Readiness Review
9.3 Solution Evaluation and Roadmap Development
9.4 Business Value and Communication
9.5 Capstone Tracks
9.2 AI Use-Case Selection and Readiness Review
9.3 Solution Evaluation and Roadmap Development
9.4 Business Value and Communication
9.5 Capstone Tracks
| Format | Online, self-paced |
|---|---|
| Duration | Instructor-Led: 1 day (live or virtual) |
| Exam | 50 questions, 70% passing, 90 minutes, online proctored exam |
| Included | Instructor-led OR Self-paced course + Official exam + Digital badge |
| Prerequisites | Basic understanding of manufacturing operations such as production, maintenance, quality, and supply chain management. Learners should also be familiar with core AI, machine learning, and automation concepts, along with the ability to interpret operational data, dashboards, metrics, and trends. Awareness of digital systems such as MES, SCADA, ERP, sensors, and connected platforms is recommended, as well as basic business analysis skills to evaluate problems, feasibility, risks, value, and expected outcomes. |
| Delivery | Projects & case studies |
| Outcome | Industry-recognized credential + hands-on experience |
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