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.,...
TensorFlow Articles
-
-
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...
-
Co-authors: Jun Shi, Mingzhou Zhou Introduction In the machine learning community, Apache Spark is widely used for data processing due to its efficiency in SQL-style operations, while TensorFlow is one of the most popular frameworks for model training. Although there are some data formats supported by both tools, TFRecord—the data format native to TensorFlow—is...
- Topics:
- Spark,
- machine learning,
- TensorFlow,
- Data,
- Open Source
-
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
-
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