MLOps – Page 3 – Andrew Fairless, Ph.D.

What I Read: ML Engineering

By Andrew Fairless on November 1, 2022September 26, 2022

https://www.shreya-shankar.com/phd-year-one/ Thoughts on ML Engineering After a Year of my PhDJuly 18, 2022Shreya Shankar “There are two types of business-critical MLEs. The first is the Task MLE…”

What I Read: Machine Learning Metadata

By Andrew Fairless on October 24, 2022September 9, 2022

https://www.kdnuggets.com/2022/08/machine-learning-metadata-store.html Machine Learning Metadata StoreBy Akruti Acharya, Technical Content WriterAugust 31, 2022 “Storing the metadata would help in retraining the same model and get the same results…. Hence, the needContinue readingWhat I Read: Machine Learning Metadata

What I Read: Is Data Scientist Still the Sexiest Job?

By Andrew Fairless on September 5, 2022August 3, 2022

https://hbr.org/2022/07/is-data-scientist-still-the-sexiest-job-of-the-21st-century Is Data Scientist Still the Sexiest Job of the 21st Century?Thomas H. Davenport and DJ PatilJuly 15, 2022 “Ten years ago we published the article “Data Scientist: Sexiest JobContinue readingWhat I Read: Is Data Scientist Still the Sexiest Job?

What I Read: Hidden Technical Debts

By Andrew Fairless on August 15, 2022July 18, 2022

https://www.kdnuggets.com/2022/07/hidden-technical-debts-every-ai-practitioner-aware.html Hidden Technical Debts Every AI Practitioner Should be Aware ofVidhi ChughJuly 7, 2022 “…technical debt is the long-term costs incurred by moving quickly in software engineering…. A large partContinue readingWhat I Read: Hidden Technical Debts

What I Read: Bundling into the Database

By Andrew Fairless on July 5, 2022June 27, 2022

https://www.ethanrosenthal.com/2022/05/10/database-bundling/ Let’s Continue Bundling into the DatabaseEthan RosenthalMay 10, 2022 “Truthfully, I just want to argue that feature stores, metrics layers, and machine learning monitoring tools are all abstraction layersContinue readingWhat I Read: Bundling into the Database

What I Read: Deploying Deep Learning

By Andrew Fairless on June 28, 2022June 27, 2022

https://thegradient.pub/lessons-from-deploying-deep-learning-to-production/ Lessons From Deploying Deep Learning To ProductionPeter Gao16.May.2022 “I used to think that machine learning was about the models. Actually, machine learning in production is about pipelines.”

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: Real World Recommendation System

By Andrew Fairless on May 25, 2022April 25, 2022

https://blog.fennel.ai/p/real-world-recommendation-system?s=r Real World Recommendation System – Part 1Nikhil GargApr 10 “Training a collaborative filtering based recommendation system on a toy dataset is a sophomore year project in colleges these days.Continue readingWhat I Read: Real World Recommendation System

What I Read: MLOps Documentation

By Andrew Fairless on February 2, 2022January 20, 2022

https://www.kdnuggets.com/2021/12/best-practices-mlops-docs.html Best Practices for MLOps DocumentationBy Nifesimi Ademoye “Why does technical documentation matter in MLOps?…. MLOps levels are defined by the extent of automation of processes in an ML pipeline,Continue readingWhat I Read: MLOps Documentation

What I Read: Maps of Model Space, Stan

By Andrew Fairless on January 5, 2022December 11, 2021

https://statmodeling.stat.columbia.edu/2021/11/19/drawing-maps-of-model-space-with-modular-stan/ Drawing Maps of Model Space with Modular StanPosted on November 19, 2021 9:50 AM by AndrewThis post is by Ryan Bernstein. “What if instead of describing one model atContinue readingWhat I Read: Maps of Model Space, Stan

Tag: MLOps