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Appen is a tool that I use in my day to day to get my data labels. That's a very critical part of the process and you guys make it very easy.
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Cullen Billhartz
Machine Learning and Computer Vision Technologist, The Boeing Company
With Appen, we’re getting trust and reliability. We can have confidence that the things we’re building are functionally right, that they do what we say they do while meeting the metrics our customers care about.
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Colin Sullivan
Data Science Manager, Brandwatch
Appen is so fast. Using their platform, we could do overnight what used to take us a month. Appen is wonderfully efficient.
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Rick Britt
Vice President of AI, CallMiner
The fact that Appen has it all right there, with a project as subjective as emotion, I can be really confident in what we’re getting back and have those numbers to back up the work that we’re doing.
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Micaela Kaplan
Data Scientist and Ethics in AI Lead, CallMiner
We were thrilled to achieve our desired level of quality at the scale that we needed on the Appen platform to train our new citation intent classification model.
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Sebastian Kohlmeier
Sr. Manager – Business Operations, Allen Institute for AI
Appen’s platform is really easy to use. What makes it great is you can reach so many different channels because of its global outreach.
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Kenneth Benoit
Director of the Data Science Institute, LSE
We had to buy critical data from Appen, and that data has been incorporated into our background lexicon. This helps us build out new vocabularies for our clients.
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Ines Wendler
Product Manager, MediaInterface
What’s really key is the guaranteed throughput more than anything, because that means I can give customers a definitive deadline, which I could never do before we started working with Appen. And I can get back to them with quality and quantitative numbers.
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Jon Morra
Chief Data Scientist
Working with Appen has made our model development process 10 times faster, allowing us to get to the next step much quicker and think about audio and video at scale.
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Product Manager at GumGum
We knew we were making progress when we could rapidly rebalance our training data and integrate feedback from our ML team into the process. The iterative cycle sped up dramatically, and we could move our models from idea to production in record time.
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Mark Lemmons
co-founder of Shotzr
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