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Imagine a system with a backend of multiple microservices. Instead of the client directly communicating with each microservice, it's more efficient to use a dedicated service (BFF - Backend for Frontend) to aggregate information and send it back to the client. However, as the system grows, determining which team should handle the BFF service becomes unclear. This is where Federated Architecture, l
At Mercari, the responsibilities of the Platform team and the service development teams are clearly distinguished. Not all service owners possess expert knowledge of Kubernetes. Also, Mercari has embraced a microservices architecture, currently managing over 1000 Deployments, each with its dedicated development team. To effectively drive FinOps across such a sprawling landscape, it's clear that th
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This checklist contains points that must be satisfied during implementation and verified prior to release. Please note that all items in the design checklist that were verified at the end of the design phase, must still be satisfied at release time (e.g. the design doc must be up-to-date, the SLOs must be consistent, ...).
This repository has been archived by the owner on Sep 21, 2022. It is now read-only.
English Korean 機械学習システム デザインパターン 機械学習システムを本番稼働させるために必要な学習、推論、運用のアーキテクチャ・デザイン・パターン集です。 目的 このドキュメントの目的は機械学習システムを本番稼働させるためのシステム・デザイン・パターンを説明することです。 このドキュメントは機械学習のモデル開発でパフォーマンスを向上させる方法(正解率やRMSE)を説明するものではありませんが、パターンによってはその手法に言及することもあります。 前提 このドキュメントで書かれる機械学習システムパターンのほとんどは、パブリック・クラウドおよびKubernetesを使って稼働させることを前提に記述されています。特定のプログラミング言語に依存しない内容にするよう努めますが、機械学習で使われる最もポピュラーな言語がPythonであるため、ほとんどのデザイン・パターンはPythonで
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