Developing, Deploying and Managing Models at Scale

There’s a vibrant ecosystem of choices available for data scientists to perform their job. This spans programming languages – such as Python, R and Java – as well as integrated development environments, deployment technologies, virtual machines, Kubernetes and more. While these choices create a lot of opportunities, they also can lead to option fatigue, resulting in an overcrowded, uneven landscape that makes it difficult for data practitioners and companies to pursue the real priorities to generate business values. In this talk, data scientist Marinela Profi will explain how ModelOps and MLOps can help you streamline and simplify the process and achieve Responsible AI. She’ll discuss the difference between the two approaches and the important role they play in solving common challenges with the ML lifecycle. Taking it a step further, she will introduce the concept of an analytics platform to develop, deploy and monitor any type of model to adopt a full life cycle approach. She’ll also discuss how to integrate different open source packages and ensure that proper model governance and audibility best practices remain in place.