Abstract: 

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 to scale analytics and create business value.

In this talk, data scientist Marinela Profi will explain how ModelOps and MLOps can help you streamline and simplify the process. 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 auditability best practices remain in place.

Bio: 

Marinela Profi uses her cross-domain expertise in statistics, business and marketing, to showcase the value of SAS Platforms and Industry Solutions specifically for the Data Science and Machine Learning field.

She is a keynote speaker for global conferences, where she shares trends and priorities of the data science industry and a published author and contributor to major industry and data science publications. Her main contributions have been around the combination of forecasting and machine learning techniques.

As a proud woman in tech, she finds many ways to empower and educate other women on why joining the tech world is inspiring and rewarding.