Co-authors: Eing Ong, Shannon Bain, and Daniel Qiu What is MLOps? Before we dive into our MLOps portal, let’s begin by defining MLOps (Machine Learning Operations). MLOps is about continuously running ML correctly by managing the full lifecycle (developing, improving, and maintaining) for AI models. A structured and methodical approach that starts at problem...
infrastructure Articles
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Co-authors: Ze Mao, Matt Wise, Casey Getz, Justin Lin, Ashish Singhai, and Rob Block Introduction Ambry is LinkedIn's scalable geo-distributed object store. Developed in-house and open sourced in 2016, Ambry stores tens of petabytes of data. At LinkedIn, Ambry is used to store objects like photos, videos, and resume uploads, as well as internal binary data....
- Topics:
- infrastructure,
- Data,
- Storage,
- Open Source
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Co-authors: Xiaoyang Gu, Xie Lu, and Xiaoguang Wang Introduction In August 2019, we introduced our members and customers to the idea of moving LinkedIn’s two core talent products—Jobs and Recruiter—onto a single platform to help talent professionals be even more productive. This single platform is called the New Recruiter & Jobs. Figure 1: New Recruiter & Jobs...
- Topics:
- infrastructure,
- scale,
- Data
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Co-authors: Konstantin V. Shvachko, Chen Liang, and Simbarashe Dzinamarira LinkedIn runs its big data analytics on Hadoop. During the...
- Topics:
- scale,
- Hadoop,
- Distributed Systems,
- infrastructure
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While site outages are inevitable, it’s our job to minimize both the duration of outages and the likelihood for an outage to occur....
- Topics:
- Performance,
- infrastructure,
- SRE
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Co-authors: Alexander Ivaniuk and Weitao Duan Editor’s note: This blog post is the second in a series providing an overview and...
- Topics:
- scale,
- A/B Testing,
- infrastructure,
- T-REX