logging – Andrew Fairless, Ph.D.

What I Read: Generative AI Platform

By Andrew Fairless on October 29, 2024August 3, 2024

https://huyenchip.com//2024/07/25/genai-platform.html Building A Generative AI PlatformChip Huyen7/24/24 “This post outlines the common components of a generative AI platform, what they do, and how they are implemented.”

What I Read: Retrieval Augmented Generation at scale

By Andrew Fairless on December 13, 2023November 7, 2023

https://medium.com/@neum_ai/retrieval-augmented-generation-at-scale-building-a-distributed-system-for-synchronizing-and-eaa29162521 Retrieval Augmented Generation at scale — Building a distributed system for synchronizing and ingesting billions of text embeddingsNeum AISep 28 “…getting a Retrieval Augmented Generation (RAG) application started isContinue readingWhat I Read: Retrieval Augmented Generation at scale

What I Read: streaming for data scientists

By Andrew Fairless on September 21, 2022August 22, 2022

https://huyenchip.com//2022/08/03/stream-processing-for-data-scientists.html Introduction to streaming for data scientistsChip HuyenAug 3, 2022 “In our recent survey, almost half of the data scientists we asked said they would like to move from batchContinue readingWhat I Read: streaming for data scientists

What I Read: Modern Stack for ML Infrastructure

By Andrew Fairless on June 13, 2022May 23, 2022

https://youtu.be/gZnhSHvhuFQThe Modern Stack for ML Infrastructure | OuterboundsMay 2, 2022Data Council “Metaflow was originally developed at Netflix to provide a user-friendly platform for a wide range of ML use cases…Continue readingWhat I Read: Modern Stack for ML Infrastructure

What I Read: Data Distribution Shifts

By Andrew Fairless on April 6, 2022March 12, 2022

https://huyenchip.com//2022/02/07/data-distribution-shifts-and-monitoring.html Data Distribution Shifts and MonitoringChip HuyenFebruary 7 2022 12:00 AM “…deploying a model isn’t the end of the process. A model’s performance degrades over time in production. Once aContinue readingWhat I Read: Data Distribution Shifts

What I Read: Lessons on ML Platforms

By Andrew Fairless on December 20, 2021November 16, 2021

https://towardsdatascience.com/lessons-on-ml-platforms-from-netflix-doordash-spotify-and-more-f455400115c7 Lessons on ML Platforms — from Netflix, DoorDash, Spotify, and moreErnest ChanMay 12 “Many companies who do ML well have invested in ML platforms to enable data scientists toContinue readingWhat I Read: Lessons on ML Platforms

What I Read: Building Data Platform

By Andrew Fairless on August 12, 2021July 22, 2021

https://www.montecarlodata.com/the-quick-and-dirty-guide-to-building-your-data-platform/ Updated July 9, 2021The Quick and Dirty Guide to Building Your Data PlatformBarr Moses, Lior Gavish“For most organizations, building a data platform is no longer a nice-to-have but aContinue readingWhat I Read: Building Data Platform

What I Read: Compare ML Experiment Tracking Tools

By Andrew Fairless on April 21, 2021April 3, 2021

https://dagshub.com/blog/how-to-compare-ml-experiment-tracking-tools-to-fit-your-data-science-workflow/ How to Compare ML Experiment Tracking Tools to Fit Your Data Science WorkflowData Science WorkflowMar 22, 2021Guy SmoilovskyCo-Founder & CTO @ DAGsHub “There are many ML experiment tracking toolsContinue readingWhat I Read: Compare ML Experiment Tracking Tools

What I Read: What is Data Observability?

By Andrew Fairless on February 20, 2021January 20, 2021

https://towardsdatascience.com/what-is-data-observability-40b337971e3e What is Data Observability?Hint: it’s not just data for DevOps.Barr MosesJun 25 “Instead of putting together a holistic approach to address data downtime, teams often tackle data quality andContinue readingWhat I Read: What is Data Observability?

What I Read: Software Tips for Data Science

By Andrew Fairless on February 2, 2021January 8, 2021

https://medium.com/swlh/software-engineering-tips-and-best-practices-for-data-science-5d85dbcf87fd Software Engineering Tips and Best Practices for Data ScienceWith great code comes great machine learningAhmed BesbesAug 28 “However, jupyter notebooks hide some serious pitfalls that may turn your codingContinue readingWhat I Read: Software Tips for Data Science

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