Moving machine learning from experimentation to production remains a challenge for many organisations. This session explores how applying DevOps principles to the MLOps lifecycle can help teams take models from notebooks into reliable, scalable production environments.

This talk will cover how practices such as version control, CI/CD, automation, and platform standardisation support reproducibility, governance, and faster iteration across the ML lifecycle. Attendees will gain practical insight into bridging the gap between data science and engineering teams, reducing deployment friction, and building MLOps workflows that scale with both data and organisational complexity.