At LinkedIn, our engineering teams build what’s needed to connect the world’s professionals to economic opportunity. For the Partner Solutions Engineering (PSE) team, this includes the technology that powers our talent and hiring solutions and support for how jobs get posted on the LinkedIn platform. Amidst the Great Reshuffle, we created multiple job ingestion...
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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...
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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