Maximising Value with MLOps

As the outputs of Machine Learning (ML) models are increasingly used and valued across industries, the need to operationalize them is greater than ever if businesses want to stay ahead of the curve and gain a competitive advantage from their data.

To deliver continuous business value from machine learning, it is essential to consider an approach that supports the quality, reliability, and overall speed of data insights. Bringing together best practices for machine learning with principles from DevOps, MLOps presents a framework for delivering ongoing insights you can trust when making business decisions.

In this recorded webcast, Thorogood Consultant Andrew Kennedy outlines our experience of using MLOps to embed analytics at an enterprise scale to explore some of our key considerations and practices to include when implementing an MLOps framework.
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