Sumanth Venkatasubbaiah

Senior Engineering Manager, Intuit

Sumanth Venkatasubbaiah is a Senior Engineering Manager at Intuit, where he is responsible for building scalable AI services and capabilities aimed at helping Intuit to become an AI-driven expert platform. He has previously built Big data and ML systems at Verizon and Apple. Sumanth holds a Masters in Computer Science from the University of North Carolina at Charlotte.

Past sessions

Summit 2021 Productionalizing Machine Learning Solutions with Effective Tracking, Monitoring, and Management

May 26, 2021 04:25 PM PT

Intuit products increasingly rely on AI solutions to drive in-product experiences and customer outcomes (a realization of Intuit’s AI-driven expert platform strategy). In order to provide complete confidence to Intuit customers through reliable and predictable experiences, we need to ensure the health of all AI solutions by continuously monitoring, managing and understanding them within Intuit products. 

At Intuit, we have deployed 100's of Machine Learning models in production to solve various problems as below:

  • Cash Flow forecasting
  • Security, risk and fraud
  • Document understanding
  • Connect customers to right agents

With so many models in production, it becomes very important to monitor and manage these models in a centralized manner. With very few open source tools available to monitor and manage ML models, data scientists find it very difficult to properly track their models. Moreover, different personas in the organization are looking for different information from the models. For example, the DevOps team is interested in operational metrics. Financial analysts are interested in determining the operational cost of a model and the legal and compliance teams might want to find if the models are explainable and privacy compliant.

At Intuit, we have designed and developed a system that tracks and monitors ML Models across the various Model development lifecycle stages. In this Summit, we will be presenting the various challenges in building such a central system. We would also share the overall architecture and the internals of this system.

In this session watch:
Sumanth Venkatasubbaiah, Senior Engineering Manager, Intuit
Pankaj Rastogi, Developer, Intuit

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Summit 2020 How Intuit uses Apache Spark to Monitor In-Production Machine Learning Models at Large-Scale

June 25, 2020 05:00 PM PT

The presentation introduces Intuit AI Model Monitoring Service (MMS). MMS is an in-house Spark-based solution developed by Intuit AI to provide ongoing monitoring for both data (statistics of model input/output etc.) and model metrics (precision, recall, AUC etc.) of in-production ML models. The project is soon to be open-source. MMS aims to tackle multiple challenges of in-production ML model monitoring:

  1. Integration of multiple data sources from different time ranges: in order to generate all metrics to monitor an in-production model, we often need to integrate multiple datasets with different schema from different time range. For example, in order to compute model metrics like AUC, the collected ground truth is always collected in a different data set with a few days or even months delay after we record the model's output data. In other cases, we might need to integrate additional dimensional data so that we can create different segments to analyze the model per segment.
  2. Reusable and extendable metric and segmentation library: it is not scalable to develop a metric/segmentation logic per model. How to create a reusable yet extendable library to hold the metric and segmentation logic is a challenging task by considering different models might have distinct data schema. Model owners are able to take advantage of MMS to create and schedule pipelines without writing any code to monitor in-production models. MMS is able to integrate generic data and also provides a programming API to be fit into a specific data schema generated by a certain ML platform. MMS also allows developers to use MMS' APIs to create reusable metric and segmentation logic in an open-contribution library. MMS pipelines are very scalable and Intuit is using MMS to integrate 10M+ rows and 1K+ columns of in-production data to generate 10K+ metrics for in-production models.'
Sumanth Venkatasubbaiah