Co-authors: Eing Ong, Shannon Bain, and Daniel Qiu What is MLOps? Before we dive into our MLOps portal, let’s begin by defining MLOps (Machine Learning Operations). MLOps is about continuously running ML correctly by managing the full lifecycle (developing, improving, and maintaining) for AI models. A structured and methodical approach that starts at problem...
Developer Productivity Articles
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We are open sourcing Feathr – the feature store we built to simplify machine learning (ML) feature management and improve developer productivity. At LinkedIn, dozens of applications use Feathr to define features, compute them for training, deploy them in production, and share them across teams. With Feathr, users reported significantly reduced time required to...
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Co-authors: Shivani Pai Kasturi and Swati Gambhir Imagine developing on your laptop, but with the computing power of the cloud! Here at LinkedIn, we were successfully able to reduce the initial setup and build times from 10-30 minutes to just 10 seconds for most of our products with a new remote development experience. In this post, we’ll describe our journey to...
- Topics:
- Azure,
- Developer Productivity
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Co-authors: Rajeev Kumar, Dhritiman Das, Saatwik Nagpal, Shubham Gupta, and Vikram Singh At LinkedIn, we leverage AI to provide a...
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Co-authors: Rashmi Jain and Sonali Bhadra Search on LinkedIn is an essential part of our members’ experience, with more than 44% of...
- Topics:
- Search,
- Developer Productivity,
- Product Design
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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