TheSequence - 🏷 Data Labeling for ML: Survey
Was this email forwarded to you? Sign up here About 45% of the time in data science projects is consumed by processing and labeling data. It’s fair to say that data labeling is one of the most expensive tasks of any machine learning project. How to work with data properly when preparing it? What are the best labeling methods and tools to use in machine learning solutions today? We keep learning from the experience gained by engineers and entrepreneurs behind the leading data labeling solutions, Toloka, Superb AI, Label Studio, and more. Today, we would like to check a few things with you via a very simple survey. It will take about 3-5 minutes. The survey invites machine learning engineers and data scientists, as well as AI enthusiasts. The level of experience is not that relevant. As a thank you, we will send you a cheat sheet with useful resources that help you understand and organize data labeling as well as a $50 promo code for your own project. Thank you! You’re on the free list for TheSequence Scope and TheSequence Chat. For the full experience, become a paying subscriber to TheSequence Edge. Trusted by thousands of subscribers from the leading AI labs and universities. |
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🗣🗣🗣Another Amazing Week for Large Language Models
Sunday, August 7, 2022
Weekly news digest curated by the industry insiders
📝 Guest post: Auto Labeling to Power Insurance Automation: Quickly Label Quality Datasets*
Friday, August 5, 2022
In this guest post, Superb AI shares a use case with their client Autonet that focuses on streamlining the claims experience in the auto insurance industry, using computer vision, AI, and domain
🗺 Edge#214: NLLB-200, Meta AI’s New Super Model that Achieved New Milestones in Machine Translations Across 200 L…
Thursday, August 4, 2022
One of the most important achievements to bring machine translation to low-resource languages
🩺 Edge#213: Testing Trained Models
Tuesday, August 2, 2022
the fundamental types of tests that can be applied to trained models +how Meta uses Bayesian Optimization for A/B tests +TensorFlow's What-If Tool
📝 Guest post: Using AI to Learn a Disentangled Gait Representation for Versatile Quadruped Locomotion*
Monday, August 1, 2022
The Oxford Robotics Institute (ORI) is built from collaborating and integrated groups of researchers, engineers and students all driven to change what robots can do for us. The ORI is interested in a
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