In episode 89 of The Gradient Podcast, Daniel Bashir speaks to Shreya Shankar.
Shreya is a computer scientist pursuing her PhD in databases at UC Berkeley. Her research interest is in building end-to-end systems for people to develop production-grade machine learning applications. She was previously the first ML engineer at Viaduct, did research at Google Brain, and software engineering at Facebook. She graduated from Stanford with a B.S. and M.S. in computer science with concentrations in systems and artificial intelligence. At Stanford, helped run SHE++, an organization that helps empower underrepresented minorities in technology.
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Outline:
* (00:00) Intro
* (02:22) Shreya’s background and journey into ML / MLOps
* (04:51) ML advances in 2013-2016
* (05:45) Shift in Stanford undergrad class ecosystems, accessibility of deep learning research
* (09:10) Why Shreya left her job as an ML engineer
* (13:30) How Shreya became interested in databases, data quality in ML
* (14:50) Daniel complains about things
* (16:00) What makes ML engineering uniquely difficult
* (16:50) Being a “historian of the craft” of ML engineering
* (22:25) Levels of abstraction, what ML engineers do/don’t have to think about
* (24:16) Observability for Production ML Pipelines
* (28:30) Metrics for real-time ML systems
* (31:20) Proposed solutions
* (34:00) Moving Fast with Broken Data
* (34:25) Existing data validation measures and where they fall short
* (36:31) Partition summarization for data validation
* (38:30) Small data and quantitative statistics for data cleaning
* (40:25) Streaming ML Evaluation
* (40:45) What makes a metric actionable
* (42:15) Differences in streaming ML vs. batch ML
* (45:45) Delayed and incomplete labels
* (49:23) Operationalizing Machine Learning
* (49:55) The difficult life of an ML engineer
* (53:00) Best practices, tools, pain points
* (55:56) Pitfalls in current MLOps tools
* (1:00:30) LLMOps / FMOps
* (1:07:10) Thoughts on ML Engineering, MLE through the lens of data engineering
* (1:10:42) Building products, user expectations for AI products
* (1:15:50) Outro
Links:
* Papers
* Towards Observability for Production Machine Learning Pipelines
* Rethinking Streaming ML Evaluation
* Operationalizing Machine Learning
* Moving Fast With Broken Data
* Blog posts
* The Modern ML Monitoring Mess
* Thoughts on ML Engineering After a Year of my PhD
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