deployment – Andrew Fairless, Ph.D.

What I Read: Musings on AI Engineering

By Andrew Fairless on September 16, 2024June 29, 2024

https://www.sh-reya.com/blog/ai-engineering-short Short Musings on AI Engineering and “Failed AI Projects”Shreya ShankarJun 24, 2024 “When we studied the traditional MLOps lifecycle, we found one common pattern across all successful ML products—theirContinue readingWhat I Read: Musings on AI Engineering

What I Read: Don’t put notebooks into production

By Andrew Fairless on June 27, 2024April 23, 2024

https://martinfowler.com/articles/productize-data-sci-notebooks.html Don’t put data science notebooks into productionDavid Johnston18 November 2020 “…the discussion of how to productionize data science notebooks is missing the point. The goal should be to empowerContinue readingWhat I Read: Don’t put notebooks into production

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: Enterprise AI, RAG + Fine Tuning

By Andrew Fairless on January 11, 2024November 20, 2023

https://www.montecarlodata.com/blog-the-moat-for-enterprise-ai-is-rag-fine-tuning/ The Moat for Enterprise AI is RAG + Fine Tuning – Here’s WhyLior GavishUpdated Nov 09 2023 “Data needs to be secure and private; LLM deployment needs to beContinue readingWhat I Read: Enterprise AI, RAG + Fine Tuning

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: LLM Apps, Data Pipelines

By Andrew Fairless on December 7, 2023November 20, 2023

https://meltano.com/blog/llm-apps-are-mostly-data-pipelines/ LLM Apps Are Mostly Data PipelinesPat NadolnyAugust 22 2023 “While the existing LLM app tools like LangChain and LlamaIndex are useful for building LLM apps… you should heavily considerContinue readingWhat I Read: LLM Apps, Data Pipelines

What I Read: Kubernetes, Batch

By Andrew Fairless on July 25, 2023June 15, 2023

https://betterprogramming.pub/kubernetes-was-never-designed-for-batch-jobs-f59be376a338 Kubernetes Was Never Designed for Batch JobsAnd what it would look like if it had beenMeadowrunSep 1, 2022 “…Kubernetes is philosophically biased towards microservices over batch jobs. This resultsContinue readingWhat I Read: Kubernetes, Batch

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

Tag: deployment