AsyncAPI and OpenAPI: an API Modeling Approach

AsyncAPI is gaining traction in the ecosystem of API tools. It solves an important problem: it provides a convenient way of describing the interface of event-driven systems independently of the underlying technology. With AsyncAPI, evented systems can be treated as any other API product: a productizable and reusable, self-describing building block encapsulating some set of… Continue reading AsyncAPI and OpenAPI: an API Modeling Approach

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Flow Scheduling for the Einstein ML Platform

At Salesforce, we have thousands of customers using a variety of products. Some of our products are enhanced with machine learning (ML) capabilities. With just a few clicks, customers can get insights about their data. Behind the scenes, it’s the Einstein Platform that builds hundreds of thousands of models, unique for each customer and product,… Continue reading Flow Scheduling for the Einstein ML Platform

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Pegasus Data Language: Evolving schema definitions for data modeling

Pegasus Data Schema (PDSC) is a Pegasus schema definition language that has been used for data modeling with Rest.li services for years. It’s the underlying language that helps define data models, describe the data returned by REST endpoints, and generate derivative schemas for other uses, such as XML schemas and various database schemas. However, writing… Continue reading Pegasus Data Language: Evolving schema definitions for data modeling

Journey to a million models

Journey to a Million Models The AIOps team in Salesforce started developing an anomaly detection system using the large amount of telemetry data collected from thousands of servers. The goal of this project was to enable proactive incident detection and bring down the mean time to detect (MTTD) and mean time to remediate (MTTR) Simple problem, right?… Continue reading Journey to a million models

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Creating a secure and trusted Jobs ecosystem on LinkedIn

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… Continue reading Creating a secure and trusted Jobs ecosystem on LinkedIn

How machine learning powers Facebook’s News Feed ranking algorithm

Designing a personalized ranking system for more than 2 billion people (all with different interests) and a plethora of content to select from presents significant, complex challenges. This is something we tackle every day with News Feed ranking. Without machine learning (ML), people’s News Feeds could be flooded with content they don’t find as relevant… Continue reading How machine learning powers Facebook’s News Feed ranking algorithm

Smart Argument Suite: Seamlessly connecting Python jobs

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… Continue reading Smart Argument Suite: Seamlessly connecting Python jobs

Budget-split testing: A trustworthy and powerful approach to marketplace A/B testing

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, however, are… Continue reading Budget-split testing: A trustworthy and powerful approach to marketplace A/B testing

How LinkedIn turned to real-time feedback for developer tooling

Over the last year, we have been using real-time feedback to evolve our tooling and provide a more productive experience for LinkedIn’s developers. It’s helped us double our feedback participation, and more importantly, better tailor our recommendations and improvements.  For any engineering organization looking to improve developer experiences, the following questions will provide a good… Continue reading How LinkedIn turned to real-time feedback for developer tooling