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LABELBOX BLUEPRINT

SIX FUNDAMENTAL SESSIONS TO IMPROVE YOUR AI DATA





LABELBOX BLUEPRINT IS A VIRTUAL EVENT DESIGNED SPECIFICALLY TO GUIDE DATA
PRACTITIONERS AND AI BUILDERS ALONG THE PATH TO MORE EFFICIENT DATA-CENTRIC
PRODUCTION AI.

THE DAY WILL FEATURE IN-DEPTH SESSIONS OFFERING PRACTICAL HELP ON SOLVING THE
CHALLENGES WE SEE MOST OFTEN ACROSS OUR CUSTOMERS.


WATCH NOW

HOW TO BUILD A DATA ENGINE FOR YOUR ORGANIZATION

Speakers: Manu Sharma (Labelbox CEO) + Peter (VP Product, OpenAI)

Watch now

AI is supposed to be transforming your organization but data leaders are finding
that it is taking much longer than anticipated for their AI investments to get
to full impact. At the heart of the problem is the process of converting
proprietary data into revenue-generating AI and the exponential need for labeled
data. To solve this, learn how leading enterprises have built an AI data engine
that allows their teams to prioritize and iterate on the right data at the right
stage of their project’s life cycle, enabling them to build lasting competitive
advantage and get to production AI faster.

HOW TO IMPROVE PERFORMANCE WITH LESS DATA

Speakers: Will Van Eaton (Product marketing) + Maxime Voisin (Product)

Watch now

Machine learning has evolved past the traditional method of iterating on models
with a single large training dataset. Today, leading ML teams extend their focus
to carefully selecting their training data, training the model, examining its
performance, and modifying the next training dataset accordingly. Learn how to
conduct model error analysis and data curation, both of which are vital to
data-centric ML.

HOW TO UPGRADE YOUR DATA QA PROCESS

Speakers: Chris Amata (Solutions engineering) + Pranoy Chaudhuri (Product)

Watch now

Preparing training data for real-world models requires a sophisticated quality
assurance plan, with considerations of multiple passes, consensus
decision-making, gold data insertions, etc. Learn how to utilize the right
workflows and infrastructure to get a real-time view of data quality and labeler
performance.

HOW TO COLLABORATE IN LOCKSTEP WITH YOUR LABELING OPS

Speakers: Marley Jones (Labeling Ops) + Jake Pellerin (Labeling Ops)

Watch now

As more ML teams shift their focus from iterating on models to improving the
quality of their training data, labeling operations is becoming a central pillar
of their larger MLOps strategy. Creating, managing, and maintaining an efficient
labeling pipeline that produces high-quality training data is an underestimated
challenge. Learn how leading AI teams can improve their labeling operations to
deliver accurate and scalable training data by emphasizing better collaboration,
transparency and communication.

HOW TO BUILD A STRONG FOUNDATION FOR ML WHEN YOU DON’T YET HAVE A MODEL

Speakers: Karen Yang (Product)+ Rob Wood (Solutions engineering)

Watch now

Learn how you can derive significant value from building a foundation to manage
your in-house data and IP, even if your enterprise is months or years from
deploying AI. This session will also cover how AI teams can more easily train,
evaluate, and improve ML models by combining these workflows in their training
data platform.

HOW TO PROPERLY LEVERAGE LABELING AUTOMATION

Speakers: Jenna Wang (Product)

Watch now

Learn how to dramatically reduce labeling time using the latest in automation
from specialied annotation tools to model-assisted labeling. See what workflows
can help you increase labeling velocity whether you’re actively iterating on
production models or are just getting started on your AI journey. Teams using
model-assisted labeling have seen a savings of 50-70% in terms of annotation
costs and can dramatically improve overall project efficiency.

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