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Labelbox Academy: Mastering the platform

June 30th, 8am-11am PST

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Join us to sharpen your use of the Labelbox platform and walk away ready to leverage our most advanced and extensible capabilities.

Learn how to increase labeling velocity, reduce cost, and shorten time to production using features and workflows designed to accelerate model performance improvements. See how leveraging the Python SDK, labeling automation, and advanced workflows can help you build the fastest iteration loop.

You’ll learn how to:

Make labeling easier and faster with model-assisted labeling. Using your own model can drive 50-70% savings in annotation costs and faster iteration.

Use the Python SDK to access all functionalities of the Labelbox API. Speed up workflows like configuring labeling projects, managing your review queue, and adding attachments without having to use GraphQL.

Ensure you’re consistently labeling the most impactful data by using active learning frameworks and labeling queue customization.

Schedule

All times in PDT

8:00-8:45

Fireside Chat with Anna Patterson

Learn what it takes to build a truly robust AI system from a former VP of Engineering at Google and a founder of multiple successful AI startups. Anna will also share her perspective on some of the most exciting directions for AI today.

8:45-9:30

Accelerate your training data pipeline with model-assisted labeling

Your own model is your most powerful tool for dramatically improving the speed and accuracy of your labeling pipeline. Whether you’re just getting started with an open source model or you’re ironing out the long tail of edge cases, model-assisted labeling workflows can help you get to production faster.

Setting your project up for success: the secret to efficient labeling operations

Labelbox’s COO, Brian Rieger, will teach you how to optimize your ML operations and improve accuracy through effective collaboration. Apply best practices from manufacturing and software development to reduce cost, increase quality, and get to production faster.

9:30-10:15

Monitor performance of deployed models

Build dashboards and workflows for monitoring production models by combining Labelbox with readily available open source tools. Calculate and track performance metrics by comparing model predictions on production data to ground truth labels created in real time.

Introduction to Labelbox

See a live demo of some of the basic functionalities of our training data platform, including annotation tools and quality management features.

10:15-11:00

Next big thing from Labelbox

Join Labelbox’s CEO, Manu Sharma, for an unveiling of our latest offering for helping ML practitioners radically accelerate and simplify improving model performance. Attendees will get the first public preview and early access.

Speakers

  • Anna Patterson

    Managing Partner, Gradient Ventures

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  • Manu Sharma

    CEO, Labelbox

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  • Brian Rieger

    COO, Labelbox

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  • Audrey Smith

    Director of Labeling Operations, Labelbox

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  • Chris Amata

    Solutions Engineer,Labelbox

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  • Matt Sokoloff

    Machine Learning Engineer, Labelbox

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  • Gareth Jones

    Product Manager, Labelbox

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  • Erin Liu

    Machine Learning Support Engineer, Labelbox

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  • Caroline Magg

    Senior AI/ML Engineer, ImageBiopsy Lab

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