Machine Learning Operations for IT

In a rapidly evolving AI landscape, IT professionals need to understand how to effectively manage and operationalise AI projects from an IT perspective. This one-day course introduces MLOps and the key practices required to deploy, monitor, and govern AI systems effectively.
It covers the end-to-end lifecycle of machine learning, highlighting the importance of data readiness, quality, and governance, as well as model evaluation and continuous monitoring for both traditional and generative AI.
Participants will explore infrastructure and deployment considerations, along with strategic vendor decisions (Build, Buy, or Partner). The course also examines the regulatory and ethical landscape, including GDPR, the EU AI Act, and issues of bias and fairness, equipping participants to implement scalable, compliant, and trustworthy AI solutions.

What Will I Learn?

After completing the course participants will be able to:

• Understand how MLOps supports the end-to-end lifecycle of AI systems in production
• Apply best practices for data quality, governance, and compliance
• Evaluate and monitor both traditional ML and Generative AI models
• Identify and manage risks such as model drift, bias, and performance degradation
• Assess infrastructure and deployment options (cloud vs on-premise)
• Make informed vendor and delivery decisions (Build, Buy, or Partner)

Is This Course for Me?

This course is designed for IT professionals and decision-makers responsible for supporting, deploying, or governing AI systems within their organisation.

How will I learn?

This course is led by expert instructors and delivered online, featuring a dynamic blend of demonstrations and interactive workshops.