Co-authors: Oliver Tse, Andrew Lee, Melanie Sumner, Renato Iwashima Accessibility (A11y) Engineering at LinkedIn aims to streamline accessible product development and maintenance. We think about how tooling can help achieve accessibility success and increase the efficiency of our fellow engineers across the organization. We design tools and the infrastructure...
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- Topics:
- Accessibility
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Co-authors: Siddharth Dangi, Johnson Jia, Manas Somaiya, and Ying Xuan The LinkedIn feed is the cornerstone of the member experience. It’s where our members post ideas, career news, questions, and jobs in an array of formats, including short text, long-form articles, images, and videos. The Feed AI Team’s mission is to help LinkedIn’s members discover the most...
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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 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
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The product ecosystem at LinkedIn is vast, and managing its infrastructure can be daunting. Nearly 10 years ago, we transitioned away...
- Topics:
- Developer Productivity
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Built at LinkedIn, Pinot is an open source, distributed, and scalable OLAP data store that we use as our de-facto near-real-time...
- Topics:
- Pinot,
- metrics,
- Data,
- Open Source
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The needs of our members and customers have evolved rapidly in recent weeks, and we’ve been focused on evolving alongside them to meet...
- Topics:
- Developer Productivity,
- Product Design