MLOps – Page 2 – Andrew Fairless, Ph.D.

What I Read: Optimizing Machine Learning Training Pipelines

By Andrew Fairless on March 15, 2023February 4, 2023

https://medium.com/ntropy-network/bag-of-tricks-for-optimizing-machine-learning-training-pipelines-4f8d5cd3d432 Bag of tricks for optimizing machine learning training pipelinesArseny KravchenkoJan 5 “…we are constantly looking for ways to improve the efficiency of our machine learning pipelines, while keeping theContinue readingWhat I Read: Optimizing Machine Learning Training Pipelines

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 User Actions in Recommendation

By Andrew Fairless on February 27, 2023January 9, 2023

https://medium.com/pinterest-engineering/how-pinterest-leverages-realtime-user-actions-in-recommendation-to-boost-homefeed-engagement-volume-165ae2e8cde8 How Pinterest Leverages Realtime User Actions in Recommendation to Boost Homefeed Engagement VolumeXue Xia, Software Engineer, Homefeed Ranking; Neng Gu, Software Engineer, Content & User Understanding; Dhruvil Deven Badani,Continue readingWhat I Read: Realtime User Actions in Recommendation

What I Read: Building “Copilot for X”

By Andrew Fairless on February 23, 2023January 9, 2023

https://lspace.swyx.io/p/what-building-copilot-for-x-really What Building “Copilot for X” Really TakesA “Copilot for X” guide from the team that built the first real Copilot competitor!swyx and Anshul RamachandranDec 19 ““Copilot for X” refersContinue readingWhat I Read: Building “Copilot for X”

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: ML Observability

By Andrew Fairless on February 7, 2023December 4, 2022

https://medium.com/at-the-front-line/ml-observability-hype-or-here-to-stay-acef064ff843 ML Observability — Hype or Here to Stay?RuthOct 17 “Machine Learning Operations (MLOps) infrastructure is evolving at a phenomenal pace, and ML Observability is becoming a critical component ofContinue readingWhat I Read: ML Observability

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 learn: How, learn machine learning

By Andrew Fairless on January 18, 2023December 4, 2022

https://vickiboykis.com/2022/11/10/how-i-learn-machine-learning/ How I learn machine learningVicki BoykisNov 10 2022 “My overarching goal as an MLE is to continuously work towards designing and deploying well-designed, and transparent machine learning systems andContinue readingWhat I learn: How, learn machine learning

What I Read: The Farama Foundation

By Andrew Fairless on December 22, 2022November 5, 2022

https://farama.org/Announcing-The-Farama-Foundation Announcing The Farama FoundationThe future of open source reinforcement learning25 October 2022 “This means that the barrier to reinforcement learning seeing widespread deployment is a tooling problem…. Our grandContinue readingWhat I Read: The Farama Foundation

What I Read: Dev and Data Science Independence

By Andrew Fairless on December 13, 2022November 5, 2022

https://medium.com/riskified-technology/how-we-enabled-dev-and-data-science-independence-with-clear-api-boundaries-3a6a28597746 How We Enabled Dev and Data Science Independence With Clear API Boundaries Using Airflow and DatabricksUri BrodskyOct 18 “In this post, I will show you how we enabled ourContinue readingWhat I Read: Dev and Data Science Independence

Tag: MLOps