MLOps – Andrew Fairless, Ph.D.

What I Read: Compound AI Systems

By Andrew Fairless on April 22, 2024March 1, 2024

https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/ The Shift from Models to Compound AI SystemsMatei Zaharia, Omar Khattab, Lingjiao Chen, Jared Quincy Davis, Heather Miller, Chris Potts, James Zou, Michael Carbin, Jonathan Frankle, Naveen Rao, AliContinue readingWhat I Read: Compound AI Systems

What I Read: Deploy Model

By Andrew Fairless on April 9, 2024March 1, 2024

https://outerbounds.com/blog/the-many-ways-to-deploy-a-model/ The Many Ways to Deploy a ModelHamel Husain, The Outerbounds TeamFebruary 6, 2024 “Over the past years, we have been helping companies deploy a wildly diverse set of MLContinue readingWhat I Read: Deploy Model

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: MLOps, Data Engineering

By Andrew Fairless on May 18, 2023May 1, 2023

https://www.cpard.xyz/posts/mlops_is_mostly_data_engineering/ MLOps is Mostly Data Engineering.22 March 2023Kostas Pardalis “The question here is… why do we need a whole new category of products?… The true need here is developer toolingContinue readingWhat I Read: MLOps, Data Engineering

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: MLOps