Co-authors: Jonathan Hung, Pei-Lun Liao, Lijuan Zhang, Abin Shahab, Keqiu Hu TensorFlow is one of the most popular frameworks we use to train machine learning (ML) models at LinkedIn. It allows us to develop various ML models across our platform that power relevance and matching in the news feed, advertisements, recruiting solutions, and more. To ensure the best...
TensorFlow Articles
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- Topics:
- TensorFlow,
- Open Source
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Co-authors: Ian Ackerman and Saurabh Kataria Editor’s Note: Multi-objective optimization (MOO) is used for many products at LinkedIn (such as the homepage feed) to help balance different behaviors in our ecosystem. There are two parts to how we work with multiple objectives: the first is about training high-fidelity models to predict member behavior (e.g.,...
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Co-authors: Sneha Chaudhari, Mahesh Joshi, and Gungor Polatkan In part 1 of this series, we shared a high-level overview of our course recommendation engine for LinkedIn Learning. First, we provided details on the offline and online components of the system design. Later on, we discussed the three main components of the recommendation engine that are key for...
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Co-authors: Jun Shi, Mingzhou Zhou Introduction In the machine learning community, Apache Spark is widely used for data processing due...
- Topics:
- Spark,
- machine learning,
- TensorFlow,
- Data,
- Open Source
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Co-authors: Xuhong Zhang, Chenya Zhang, and Yiming Ma Today, we are announcing a new open source project called Avro2TF. This project...
- Topics:
- Spark,
- machine learning,
- TensorFlow,
- Data,
- Open Source
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Co-authors: Jonathan Hung, Keqiu Hu, and Anthony Hsu LinkedIn heavily relies on artificial intelligence to deliver content and create...
- Topics:
- Hadoop,
- machine learning,
- TensorFlow,
- Data,
- Open Source