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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems.

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

The MLOps Blog

The DevOps and Automation Ops departments are under the infrastructure team. They also need to monitor and see changes in the data distribution ( data drift, concept drift , etc.) The infrastructure team focuses on technology and delivers tools that other teams will adapt and use to work on their main deliverables.

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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. Jeff Magnusson has a pretty famous post about engineers shouldn’t write ETL. ML platform team can be for this DevOps team.

ML 52