Introduction Once you have deployed your machine learning model to production it rapidly becomes apparent that the work is not over. In many ways the journey is just beginning. How do you know if your models are behaving as you expect them to? What about next week/month/year when the customer (or fraudster) behavior changes and your training data is stale? These are complex challenges, compounded
To realize the full benefits of SRE, organizations need well-thought out reliability targets known as service level objectives (SLOs) that are measured by service level indicators (SLIs), a quantitative measure of an aspect of the service. As examined in this report, these measurable goals set forth in an organization’s SLOs eliminate the conflicts inherent in change management and event handling
※この投稿は米国時間 2019 年 1 月 26 日に Google Cloud blog に投稿されたものの抄訳です。 このたび、『The Site Reliability Workbook』がウェブサイトで閲覧できるようになりました。Google で生まれ、他の企業にも広まりつつある Site Reliability Engineering(SRE)は、運用上の問題をソフトウェア的に解決するためのエンジニアリングであり、Google におけるエンジニアリングの本質的な部分を占めています。 SRE は考え方であり、一連のプラクティスやメトリクスであり、システムの信頼性を保証するための処方箋でもあります。SRE モデルを構築すれば、サービスの信頼性が向上し、運用コストが下がり、人間が行う作業の価値が高くなって、サービスとチームの双方で大きなメリットが得られます。上述の新しいワークブックは、
Deploys require a careful balance of speed and reliability. At Slack, we value quick iteration, fast feedback loops, and responsiveness to customer feedback. We also have hundreds of engineers who are trying to be as productive as possible. Keeping to these values while growing as a company means continual refinement of our deployment system. We had to invest in greater visibility and reliability
the morning paper a random walk through Computer Science research, by Adrian Colyer Made delightfully fast by strattic Meaningful availability, Hauer et al., NSDI’20 With thanks to Damien Mathieu for the recommendation. This very clearly written paper describes the Google G Suite team’s search for a meaningful availability metric: one that accurately reflected what their end users experienced, and
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