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AI+ Manufacturing Practitioner™

AI+ Manufacturing Practitioner™

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.

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Certificate Code

AP - 5501

Exam Format

AI-Driven Remote Exam Proctoring

Course Overview

Important details and certification information

Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
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.
50 questions, 70% passing, 90 minutes, online proctored exam

Certification Modules

  1. 1.1 AI Fundamentals in Manufacturing
  2. 1.2 AI Across Plant Operations
  3. 1.3 Human and Business Context of AI Adoption
  4. 1.4 Use-Cases
  5. 1.5 Case Studies
  6. 1.6 Hands-On

  1. 2.1 Vision AI in Manufacturing
  2. 2.2 Maintenance and Reliability AI
  3. 2.3 Operational AI in Manufacturing
  4. 2.4 AI in Planning and Automation
  5. 2.5 Use-Cases
  6. 2.6 Case Studies
  7. 2.7 Hands-On Exercise

  1. 3.1 Types of Manufacturing Data
  2. 3.2 Data Readiness Requirements
  3. 3.3 Common Readiness Challenges
  4. 3.4 Use-Cases
  5. 3.5 Case Studies
  6. 3.6 Hands-On Exercise: Manufacturing KPI Dashboard Creation using Looker Studio

  1. 4.1 Deployment Approaches for Industrial AI
  2. 4.2 AI System Structure
  3. 4.3 Integration and Solution Evaluation
  4. 4.4 Use-Cases
  5. 4.5 Case Studies
  6. 4.6 Hands-On Exercise: AI System Architecture Mapping Exercise using Miro or draw.io

  1. 5.1 Identifying and Prioritizing AI Opportunities
  2. 5.2 Pilot and Proof-of-Concept Design
  3. 5.3 Measuring and Scaling AI Impact
  4. 5.4 Real-World Implementation Constraints
  5. 5.5 Use-Cases
  6. 5.6 Case Studies
  7. 5.7 Hands-On Exercise: AI Pilot and Implementation Roadmap Workshop using Miro

  1. 6.1 Responsible AI in Industrial Operations
  2. 6.2 Governance and Data Responsibility
  3. 6.3 Security and Safety Risks
  4. 6.4 Human Oversight and Escalation
  5. 6.5 Use-Cases
  6. 6.6 Case Studies
  7. 6.7 Hands-On Exercise: AI Risk and Governance Checklist Exercise using Google Sheets

  1. 7.1 AI Project Failures in Manufacturing
  2. 7.2 Success Patterns in AI Adoption
  3. 7.3 ROI Frameworks for Manufacturing AI
  4. 7.4 Industry Comparison
  5. 7.5 Use-Cases
  6. 7.6 Case Studies
  7. 7.7 Hands-On Exercise: AI ROI Estimation and Benefit Tracking

  1. 8.1 Emerging AI Directions in Manufacturing
  2. 8.2 Digital Twins and Intelligent Monitoring
  3. 8.3 Generative AI in Manufacturing
  4. 8.4 Future Adoption Outlook
  5. 8.5 Use-Cases
  6. 8.6 Case Studies
  7. 8.7 Hands-On: AI Adoption Roadmap Creation

  1. 9.1 Problem Definition and Scope
  2. 9.2 AI Use-Case Selection and Readiness Review
  3. 9.3 Solution Evaluation and Roadmap Development
  4. 9.4 Business Value and Communication
  5. 9.5 Capstone Tracks

AI Tools Covered

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
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