- Machine Learning
- Data Science
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.
Come along to our free webcast to understand the value of MLOps, exploratory analytics, and why it should be a priority to adopt for all businesses using AI and ML models.
What we’ll 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?