How AI Happens Credit Karma VP Engineering Vishnu Ram
Vishnu provides valuable advice for data scientists who want to help create high-quality data that can be used effectively to impact business outcomes. Tune in to gain insights from Vishnu's extensive experience in engineering leadership and data technologies.
Key Points From This Episode:
- An introduction to Vishnu Ram, his background, and how he came to Credit Karma.
- His prior exposure to AI in the form of fuzzy logic and neural networks.
- What Credit Karma needed to do before the injection of AI into its data functions.
- The journey of building Credit Karma into the data science operation that it is.
- Challenges of building the models in time so the data isn’t outdated by the time it can be used.
- The nature of technical debt
- How compensating for technical debt with people or processes is different from normal business growth.
- The current data culture of Credit Karma.
- Some pros and cons of a multi-team approach when introducing new platforms or frameworks.
- The process of adopting TensorFlow and injecting it in a meaningful way.
- How they mapped the need for this new model to a business use case and the internal education that was needed to make this change.
- Insight into the shift from being an individual contributor into a management position with organization-wide challenges.
- Advice to data scientists wanting to help to create a data culture that results in clean, usable, high-quality data.
Tweetables:
“One of the things that we always care about [at Credit Karma] is making sure that when you are recommending any financial products in front of the users, we provide them with a sense of certainty.” — Vishnu Ram [0:05:59]
“One of the big things that we had to do, pretty much right off the bat, was make sure that our data scientists were able to get access to the data at scale — and be able to build the models in time so that the model maps to the future and performs well for the future.” — Vishnu Ram [0:08:00]
“Whenever we want to introduce new platforms or frameworks, both the teams that own that framework as well as the teams that are going to use that framework or platform would work together to build it up from scratch.” — Vishnu Ram [0:15:11]
“If your consumers have done their own research, it’s a no-brainer to start including them because they’re going to help you see around the corner and make sure you're making the right decisions at the right time.” — Vishnu Ram [0:16:43]
Links Mentioned in Today’s Episode:
TFX: A TensorFlow-Based Production-Scale Machine Learning Platform [19:15]