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.

Machine Learning Operations, also known as MLOps, is a framework that streamlines the process of automation, deployment, maintenance, and overall productionization of ML models. This productionization facilitates the quality, reliability, and overall speed to data insights to continuously provide business value.

In this recorded webcast Thorogood Consultants Emily Dentinger and Sarah Diehl explore some of our key considerations and practices to consider when implementing an MLOps framework that we have seen to be important.

What we cover:

• An introduction to exploratory analytics and its relationship to MLOps
• 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 derive more value from your outputs?
• Have you been hearing about MLOps and are unsure of what it is?
• Would you like to learn about the business value of MLOps, and how Machine Learning at-scale can support decision making?