Industrial AI — From Proof of Concept to Production
A two-day program on building, scaling, and governing industrial AI systems that deliver measurable operational outcomes.
Course Overview
Many industrial AI projects succeed in proof of concept but fail to reach production. This program addresses the full lifecycle of industrial AI — from identifying viable use cases and building robust PoCs to scaling to production and maintaining AI systems in industrial environments. Participants learn the technical, organizational, and operational considerations that determine whether an AI initiative succeeds at scale.
Target Audience
Industrial operations and engineering professionals
Prerequisites
Familiarity with industrial operations. Basic understanding of data and analytics is helpful.
Learning Objectives
- Identify viable industrial AI use cases
- Design and execute effective proof of concepts
- Navigate the path from PoC to production
- Address data, infrastructure, and MLOps challenges
- Govern and maintain AI systems in production
Key Modules
- Industrial AI Use Case Identification
- Building Effective Proof of Concepts
- Data Strategy for Industrial AI
- MLOps and Model Lifecycle Management
- Scaling AI in Industrial Environments
- AI Governance, Monitoring, and Maintenance
- Change Management for AI Adoption
Learning Outcomes
- A framework for evaluating industrial AI use cases
- Understanding of the PoC-to-production journey
- Practical knowledge of MLOps for industrial AI
- A roadmap for scaling AI in your operations
Your Instructor
Dr. Hussein Mustapha
AITECH Academy Faculty
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