- Data Science
- Machine Learning
Machine Learning Operations (commonly referred to as MLOps), enables organizations to maximize the return on their Data Science investments. MLOps allows organizations to streamline the industrialization of ML experiments or experiment in a more robust and scalable manner, it emphasizes reusability, and it simplifies processes surrounding Data Science.
Embarking on a journey to broadly adopt MLOps practices within your technology function can be daunting, but establishing a few key foundations can simplify and streamline this shift in Data Science approach. In this 30-minute webinar, Thorogood will share a framework that we believe enables enterprise organizations to realize the value of MLOps. We look at a few key components of an MLOps rollout strategy as they pertain to People, Processes, Tools, and Data.
In this recorded webinar Thorogood Consultants John Miller and Alaistair Jones explain the value of MLOps, and then how to successfully embrace and inculcate MLOps using a tested framework.
What we’ll cover:
- An overview of MLOps and the value it provides enterprise organizations
- A framework to assist with the successful adoption of MLOps within an organization that considers: People, Data, Processes and Tools
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?
- Would you like to learn about how some organizations are structuring themselves for MLOps success?