Quantitative Statistics for Data Cleaning

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