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...
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Venice, which was developed in late 2015, is a key-value store platform built for serving read-heavy workloads and optimized for serving derived data. Since being deployed to production in 2016, it has become very popular in the recommendation world to serve derived datasets inside LinkedIn. Venice handles single-get and small batch-get requests very efficiently...
- Topics:
- Performance,
- Venice,
- scalability
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Co-authors: Ameen Maali and Surbhi Jain At LinkedIn, our security team strives to provide a safe and secure experience for our 830M members and customers by quickly addressing security vulnerabilities, constantly improving our defenses, and safeguarding our product development process. Since 2014, our private bug bounty program with HackerOne, which connects...
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Co-authors: Joshua Shinavier and Shirshanka Das Data governance is easy… as long as the data to be governed is small and simple. A...
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At LinkedIn, our engineering teams are constantly working to keep the company at the cutting edge of innovation to deliver value for...
- Topics:
- artificial intelligence,
- Recommender Systems,
- nlp
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Co-Authors: Alex Tsun, Bo Ling, Nikita Zhiltsov, Declan Boyd, Benjamin Le, Aman Grover, and Daniel Hewlett Introduction One major goal...
- Topics:
- machine learning