Manasi Vartak

Founder and CEO

Manasi started Verta.AI (https://verta.ai) based on her PhD work at MIT CSAIL on systems for software to streamline the process of data science and machine learning.

Previously, she was a PhD student in the Database Group at MIT. She worked on systems for analysis of large scale data, specifically on making machine learning and visual analysis faster, interactive, and more efficient. She also worked/interned at Twitter, Google, Facebook and Microsoft. She is the recipient of the Facebook PhD Fellowship and Google Anita Borg Fellowship.

Tuesday, October 1
11:50 am
12:15 pm
Robertson 2
Models are the new code. While machine learning models are increasingly being used to make critical product and business decisions, the process of developing and deploying ML models remain ad-hoc. In the “wild-west” of data science and ML tools, versioning, management, and deployment of models are massive hurdles in making ML efforts successful. As creators of ModelDB, an open-source model management solution developed at MIT CSAIL, we have helped manage and deploy a host of models ranging from cutting-edge deep learning models to traditional ML models in finance. In each of these applications, we have found that the key to enabling production ML is an often-overlooked but critical step: model versioning. Without a means to uniquely identify, reproduce, or rollback a model, production ML pipelines remain brittle and unreliable. In this talk, we draw upon our experience with ModelDB and Verta to present best practices and tools for model versioning and how having a robust versioning solution (akin to Git for code) can streamlining DS/ML, enable rapid deployment, and ensure high quality of deployed ML models.
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