Remove Blog Remove DevOps Remove ETL Remove Metadata
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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning Blog

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

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Real-World MLOps Examples: End-To-End MLOps Pipeline for Visual Search at Brainly

The MLOps Blog

quality attributes) and metadata enrichment (e.g., The DevOps and Automation Ops departments are under the infrastructure team. If you want to learn more about Brainly’s technology ecosystem, check out their technology blog. On top of the teams, they also have departments.

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Learnings From Building the ML Platform at Stitch Fix

The MLOps Blog

At a high level, we are trying to make machine learning initiatives more human capital efficient by enabling teams to more easily get to production and maintain their model pipelines, ETLs, or workflows. If you ever want to know some interesting stories about techniques and things, you can look up the Stitch Fix Multithreaded blog.

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