IND-102
MLOps for Industrial AI Systems
A two-day program on deploying, monitoring, and maintaining ML models in industrial production environments.
2 days
ML engineers, data engineers, DevOps engineers, AI technical leads
advanced
hybrid
AvailableOverview
Course Overview
Building an ML model is only the beginning. Industrial AI systems require robust MLOps practices to ensure reliability, reproducibility, and performance over time. This program covers the full MLOps lifecycle — from model packaging and deployment to monitoring, retraining, and governance — with specific attention to the challenges of industrial environments.
Target Audience
ML engineers, data engineers, DevOps engineers, AI technical leads
Prerequisites
IND-101 or equivalent. Programming and ML experience.
Learning Objectives
- Design an MLOps pipeline for industrial AI
- Deploy and serve ML models in production
- Monitor model performance and detect drift
- Automate retraining and model updates
- Implement model versioning and governance
Key Modules
- MLOps Fundamentals for Industrial AI
- Model Packaging and Deployment
- Serving and Inference Infrastructure
- Model Monitoring and Drift Detection
- Automated Retraining Pipelines
- Model Versioning, Registry, and Governance
- MLOps for Edge and Distributed Systems
Learning Outcomes
- A complete MLOps pipeline design
- Practical skills for model deployment and serving
- Understanding of drift detection and monitoring
- A model governance framework
Instructor
Your Instructor
Dr. Hussein Mustapha
AITECH Academy Faculty
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