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 to 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.

What we cover:

• The value available by using ML to explore data, and how designing for repeated use and automation multiplies that value
• Where people struggle with realizing the value from investments they make into exploratory analytics and machine learning, and how that can be avoided
• The importance of a well-defined MLOps framework and the benefits it will bring to your business

Is it for you?

• Are you currently investing in machine learning and/or exploratory analytics and are looking to deliver more value from your outputs?
• Are you interested to understand the key considerations for how to use machine learning to make better decisions?
• Would you like to learn how automated, repeatable analytics allows you to increase the value from your machine learning?