Co-authors: Sakshi Jain, Grace Tang, Gaurav Vashist, Yu Wang, John Lu, Ravish Chhabra, Shruti Sharma, Dana Tom, and Ranjeet Ranjan LinkedIn’s vision is to connect every member of the global workforce to economic opportunity. A key driver towards this vision is our world-class hiring marketplace, where we help job seekers find their next dream role and help...
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- Topics:
- trust engineering,
- Security
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Co-authors: Jun Jia and Alice Wu Introduction It’s a very common scenario that an AI solution involves composing different jobs, such as data processing and model training or evaluation, into workflows and then submitting them to an orchestration engine for execution. At large companies such as LinkedIn, there may be hundreds of thousands of such executions per...
- Topics:
- Python,
- machine learning,
- Open Source
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Co-authors: Min Liu, Vangelis Dimopoulos, Elise Georis, Jialiang Mao, Di Luo, and Kang Kang The LinkedIn ecosystem drives member and customer value through a series of marketplaces (e.g., the ads marketplace, the talent marketplace, etc.). We maximize that value by making data-informed product decisions via A/B testing. Traditional A/B tests on our marketplaces,...
- Topics:
- experimentation,
- Data,
- A/B Testing
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Over the last year, we have been using real-time feedback to evolve our tooling and provide a more productive experience for...
- Topics:
- Developer Productivity,
- tools
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Co-authors: Zihan Li, Sudarshan Vasudevan, Lei Sun, and Shirshanka Das Data analytics and AI power many business-critical use cases at...
- Topics:
- Stream Processing,
- Hadoop,
- Data,
- batch processing,
- Open Source,
- Gobblin,
- Kafka
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Co-authors: Alexander Ivaniuk and Weitao Duan Editor’s note: This blog post is the second in a series providing an overview and...
- Topics:
- scale,
- A/B Testing,
- infrastructure,
- T-REX