Labelbox

“As a CTO, you usually only usually hear about the technologies your team uses when things are going wrong. With Labelbox however, the only time I dealt with it is the day I signed the contract. Since then, my team has been using the technology seamlessly without any issues, and this gives me a lot of peace of mind as a technical and engineering leader.”

“We were very pleasantly surprised by how easy [Labelbox] is to use. People got the hang of it just like that. And it's not just about how easy it is to evaluate data. We really like its ability to distribute data to multiple people and keep track of what's happening — it gives us a base of operations to help traverse through all that data….but the real beauty of it, the reason we really love Labelbox, is that it integrates so well with our tech. From a thousand-foot view, it looks like an AI labeling engine, which feeds into the deep learning system and spits out trained networks.”

“Before, we were averaging roughly 29 cents per data point. This year with Labelbox, it’s down 48% to only 15 cents per data point. We essentially labeled more data for less money with an average increase in accuracy of over 20%”

“Being able to reduce the data requirement is huge because you can see the same amount of improvement in the model’s performance in half the time and with half the effort. That was enabled through targeting the model’s weaknesses with Model Diagnostics and then being able to prioritize the right data through Labelbox. Before, if we were doing a scattershot data collection, we would have been roughly processing twice as much data and with twice as much effort,” said Barrell.”

“Before using Model Diagnostics in Labelbox to target the model’s weaknesses, we had to visualize the predictions on our own and everything was much more manual,” said Noé Barrell, ML Engineer at Deque. “We had to calculate all these metrics on our own, and it was a disjointed and difficult process. Being able to convert this workflow into something we could now do simply within Labelbox made diagnosing errors a much more streamlined experience. It’s become so much easier to iterate.”

“At the highest level, we now have a way to tackle something that used to be very painful—an automatic labeling loop that lets us evaluate our speech systems daily on production data. This loop allows us to continuously retrain our models, which has led to significant improvements. For example, we saw model accuracy improvements of 35% after using Labelbox, and what effectively translated into a 2x increase in speed in terms of accelerating model development.”

“Before using Labelbox, we struggled with managing our training data creation infrastructure and manual experiment tracking. We’re now able to concentrate on model building and deployment, without sparing engineering effort and are able to speed up model training by over 10X.”

“We're packaging the Labelbox solution and redistributing it across all our business units around the globe. So this is no longer just an R&D solution, it's a corporate solution,””

“Having a transparent agile platform that’s fluid to work with….has been a transformation in how we do our work, and has been very powerful in increasing the speed of iteration, which leads to faster time to market with better insights,””

“One of the things that we loved about Labelbox from the very beginning is their belief that the data is ours,””

“By using Labelbox's model-assisted workflows, we have been able to cut a full 25 hours we have been able to cut a full 25 hours from the process of generating high-quality AI data, and we’ve found that our experts have an easier time through a software-first approach,”

“There are many data tools out there but the Labelbox backend is the real differentiator. With dynamic queueing, our team of experts are never out of work, which was a major upgrade compared to our old internal tools — both from a productivity and speed-to-production point of view.”

“With Databricks Lakehouse Platform and Labelbox, we’re able to realize our full ambition of creatively led, data-driven content creation. Labelbox continually gives our team insights on how to optimize our campaigns. And Databricks helps us maintain an efficient, sector-leading analytics capability in the cloud.”

“I would have had to ask for a 10-person team just to build a product that would let us view all our imagery, let alone generate insights on it,” Collings remarked. “In our day-to-day workflow, Labelbox saves us countless hours while delivering a tremendous amount of value.”

“Before Labelbox, we could train a model pretty quickly and evaluate against validation test sets, but getting data processed and reviewed took forever. Having a strong collaborative expert feedback service helped us get to a weekly iteration cycle”