More and more organizations, irrespective of industry, are increasingly generating business value from their data science investments. With those investments, however, there still may be something to be desired, whether it be the reproducibility, scalability, or frequency with which the outputs of these models can be ingested to drive decision making.

Machine Learning and Artificial Intelligence projects within these organizations need to evolve to continue to provide answers to ever-changing business requirements. Once a developed model produces acceptable outputs, what are the steps in taking it to the next level? The natural progression of these experiments is to productionize and operationalize these data science applications to scale and accommodate the growing appetite for predictive analytics.

There are a growing set of recommended principles in the space of MLOps (Machine Learning Operations) that ensure your organization is set up for success. A successful implementation of an MLOps framework is certainly technical by nature, but there are also considerations relating to organization structure and ways of working.

In this recorded webcast, we explore some of our key considerations and best practices for implementing an MLOps framework.

What we cover:

• An introduction to MLOps and learnings from our practical experience
• Important design considerations and best practices

Is it for you?

• Have you heard about MLOps and are you wondering how it could fit in your organization?
• Do you have experiments that you are looking to bring into a newly defined MLOps framework?
• Are you looking for recommendations as to how to scale your ML practice?