batch – Andrew Fairless, Ph.D.

What I Read: Unify Batch and ML Systems

By Andrew Fairless on January 18, 2024November 7, 2023

https://www.kdnuggets.com/2023/09/hopsworks-unify-batch-ml-systems-feature-training-inference-pipelines Unify Batch and ML Systems with Feature/Training/Inference PipelinesBy Jim Dowling, Co-Founder & CEO, HopsworksSeptember 27, 2023 “This article introduces a unified architectural pattern for building both Batch and Real-TimeContinue readingWhat I Read: Unify Batch and ML Systems

What I Read: Feature Platforms

By Andrew Fairless on March 14, 2023February 4, 2023

https://huyenchip.com//2023/01/08/self-serve-feature-platforms.html Self-serve feature platforms: architectures and APIsChip HuyenJan 8, 2023 “The last few years saw the maturation of a core component of the MLOps stack that has significantly improved theContinue readingWhat I Read: Feature Platforms

What I Read: Realtime ML

By Andrew Fairless on February 16, 2023December 16, 2022

https://fennel.ai/blog/seven-reasons-why-realtime-ml-is/ Seven Reasons Why Realtime ML Is Here To StayNikhil GargJune 17, 2022 “…more and more top tech companies are making a larger part of their machine learning as realtimeContinue readingWhat I Read: Realtime ML

What I Read: Realtime ML Pipelines

By Andrew Fairless on February 1, 2023December 4, 2022

https://medium.com/@nparsons08/challenges-of-building-realtime-ml-pipelines-4782181425c7 Challenges of Building Realtime ML PipelinesNick ParsonsNov 18 “…as companies start introducing realtime into their ML pipelines, they are finding themselves having to weigh the trade-offs between performance, cost,Continue readingWhat I Read: Realtime ML Pipelines

What I Read: Concept Drift Without Labeled Data

By Andrew Fairless on September 26, 2022August 22, 2022

https://concept-drift.fastforwardlabs.com/ Inferring Concept Drift Without Labeled DataFF22Aug 2021 “…we explore broadly applicable approaches for dealing with concept drift when labeled data is not readily accessible. We’ll start by defining whatContinue readingWhat I Read: Concept Drift Without Labeled Data

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: Should Warehouse Be Immutable?

By Andrew Fairless on June 14, 2022May 23, 2022

https://www.kdnuggets.com/2022/05/data-warehouse-immutable.html Should The Data Warehouse Be Immutable?By Barr Moses, CEO and Co-founder of Monte CarloMay 17, 2022By Barr Moses & Chad Sanderson “As he sees it, data quality and usabilityContinue readingWhat I Read: Should Warehouse Be Immutable?

What I Read: evaluate online machine learning models

By Andrew Fairless on March 14, 2022January 20, 2022

https://maxhalford.github.io/blog/online-learning-evaluation/ The correct way to evaluate online machine learning modelsMax Halford2020-06-07 “Batch models are meant to be used when you can afford to retrain your model from scratch every soContinue readingWhat I Read: evaluate online machine learning models

What I Read: Real-time machine learning

By Andrew Fairless on March 9, 2022January 20, 2022

https://huyenchip.com//2022/01/02/real-time-machine-learning-challenges-and-solutions.html Real-time machine learning: challenges and solutionsJan 2, 2022 • Chip Huyen “Starting with a batch prediction system, we’ll discuss what’s needed for a simple online prediction system using batchContinue readingWhat I Read: Real-time machine learning

Tag: batch