MLOps for Industrial Machine Learning

Course Description

Building a machine learning model is only the beginning — deploying it reliably, monitoring its performance, and maintaining it in production is where most industrial AI initiatives succeed or fail. MLOps (Machine Learning Operations) provides the practices and tools to productionise machine learning at scale.

This course develops the understanding required to move industrial machine learning from prototype to production. Participants learn model deployment, monitoring, versioning, and the continuous processes that keep models accurate as equipment and conditions change — a critical capability given that industrial models degrade when operating conditions drift beyond their training range.

What you will achieve

By the end of this training course, participants will be able to:

• Explain the role and principles of MLOps
• Understand the machine learning production lifecycle
• Deploy machine learning models to production
• Monitor model performance and detect drift
• Apply model versioning and reproducibility
• Automate model retraining pipelines
• Understand CI/CD for machine learning
• Address the specific challenges of industrial ML in production

How the course is delivered

The course combines MLOps principles with practical industrial deployment scenarios. Participants explore the model lifecycle from deployment through monitoring and retraining, focusing on the challenges specific to industrial environments.

The programme emphasises the operational discipline required to sustain industrial machine learning in production.

Designed for

This training course is ideal for:

• Industrial data scientists and ML engineers
• Data engineers supporting ML systems
• Digital transformation and analytics teams
• Engineers deploying models to production
• Technical staff scaling industrial AI

Daily programme

Certification & accreditation