Episode 131: Data Infrastructure for Consumer Platforms, Algorithmic Governance, and Responsible AI with Krishna Gade

Show Notes(02:20) Krishna described his academic experience getting an MS in Computer Science from the University of Minnesota - where he developed efficient tools for text document clustering and pattern discovery.(05:36) Krishna recalled his 5.5 years at Microsoft working on Bing's search engine.(08:32) Krishna talked about the challenges of competing against Google Search.(10:22) Krishna shared the high-level technical and operational challenges encountered during the development and scaling phase of Twitter Search.(14:55) Krishna revealed vital lessons from building critical data infrastructure at Twitter.(17:54) Krishna touched on his time at Pinterest as the head of data engineering - leading a team working on all things data from analytics, experimentation, logging, and infrastructure.(20:05) Krishna reviewed the design and implementation of real-time analytics, ETL-as-a-Service, and an A/B testing platform at Pinterest.(24:40) Krishna unpacked the major ML model performance issues while running Facebook's feed ranking platform.(28:18) Krishna distilled lessons learned about algorithmic governance from Facebook.(31:38) Krishna provided leadership lessons from building teams that create scalable platforms and delightful consumer products on Twitter, Pinterest, and Facebook.(33:19) Krishna shared the founding story of Fiddler AI, whose mission is to build trust into AI.(37:56) Krishna unpacked the key challenges and tools in his 2019 article "AI needs a new developer stack."(40:49) Krishna discussed the evolution of MLOps over the past 4 years.(42:48) Krishna explained the benefits of using the Model Performance Management (MPM) framework to address enterprise MLOps challenges.(47:01) Krishna gave a brief overview of capabilities within Fiddler's MPM platform, such as model monitoring, explainable AI, analytics, and fairness.(50:28) Krishna highlighted research efforts inside Fiddler concerning explainability, drift metric calculation, and fairness.(53:17) Krishna discussed the challenges with monitoring for NLP and Computer Vision models.(57:18) Krishna zoomed in on Fiddler's approach to model governance for the modern enterprise.(01:02:24) Krishna distilled valuable lessons learned to attract the right people who are excited about Fiddler's mission and aligned with Fiddler's culture.(01:06:08) Krishna reflected on the evolution of Fiddler's company culture.(01:09:19) Krishna shared the challenges of finding the early design partners and defining a new category of Responsible AI.(01:12:23) Krishna gave fundraising advice to founders who are seeking the right investors for their startups.(01:14:45) Closing segment.Krishna's Contact InfoLinkedInTwitterMediumFiddler's ResourcesWebsite | LinkedIn | Twitter | YouTubeAbout | Customers | CareersAI Observability | Model Monitoring | Explainable AI | Fairness | AnalyticsBlog | Docs | ResourcesMentioned ContentPeopleGoku Mohamandas (Made With ML and Anyscale)Krishnaram Kenthapadi (Chief AI Officer & Chief Scientist at Fiddler)Books"The Hard Thing About Hard Things" (Ben Horowitz)"The Five Dysfunctions of A Team" (Patrick Lencioni)Notes

My conversation with Krishna was recorded more than a year ago. Since then, I'd recommend checking out these Fiddler's resources:

Strategic investments in Fiddler by Alteryx Ventures, Mozilla Ventures, Dentsu Ventures, and Scale Asia Ventures.Fiddler introduces an end-to-end workflow for robust Generative AI back in May 2023.Krishna's thought leadership on LLMOps and the missing link in Generative AI.

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Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

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