HumanSignal

“In addition to the type of data, it should also be noted that Label Studio is compatible with various learning modes. Within the framework of our project, the tool was used to annotate data for semi-supervised learning tasks but also for active learning for the addition of feedback loops from the output of the models for possible re-training.”

“The tool benefits from an optimal design allowing developers to integrate it into your infrastructure while maintaining a high level of scalability. The containerization layer is very useful when it comes to quickly deploying the solution in a ready development environment while guaranteeing a considerable level of security… by deploying the Label Studio tool via the native docker image, the integration becomes easier and faster. This helps us to reduce the time to market of the whole MLOps strategy.”

““Label Studio's review process, complete with commenting capabilities, has significantly enhanced the quality of our data annotations. This platform fosters clear and direct communication between annotators and reviewers, promoting a learning-oriented environment and leading to continuous improvement,””

“All of the investments we've made, including tooling, licenses, annotation, crawling, extraction, and training of machine learning models, and running the team, come at a cost. But the revenue and value we bring to our customers via Label Studio have more than compensated for it.”

“Spreadsheets didn’t scale for us. With Label Studio, we reduced average annotation time on heavy tasks from ~15 minutes to ~5 minutes per task, and structured dropdowns made labeling faster and more consistent.”

““With Prompts in the HumanSignal platform, we can explore more ideas, faster. Before pre-labeling with Prompts, our old process required a full week for research. Now we can research ten ideas in that same time.””

“Label Studio is not just an annotation tool, it’s an annotator training tool!”

“Label Studio has been useful in giving us real-time metrics to allow us to react quickly to dips in quality or productivity.”

“One of the things that impressed us the most about Label Studio was the ability to craft UIs on the fly. Before Label Studio, we had too much dependency on our data scientists. We were wasting their time asking for help just to make simple changes to our labeling tooling. Label Studio allows the annotation team to be more self-serve.”

“With Label Studio Enterprise, we've been able to bootstrap our labeling performance to near-human accuracy, transforming our data processing like never before.”

“HumanSignal's solutions not only streamlined our email categorization but also empowered us with near-instant feedback, ensuring continuous quality improvement.”

“The clarity and efficiency of the POC process with HumanSignal were remarkable. We're excited about the potential of Label Studio in revolutionizing our data annotation and active learning processes.”

“Our product depends on our ability to efficiently generate a large volume of novel, accurately-labeled ultrasound imagery," explained Robhy Bustami, CEO of BioticsAI. "There are no publicly available prenatal ultrasound datasets that meet our needs in terms of volume and diversity, so we had to start from scratch and build a proprietary dataset.”

“We need highly qualified annotators, typically medical students, to label the bulk of the data, as well as board-certified physicians that specialize in medical imaging to review the annotators' work and sign off on our datasets.”

“At the end of the day, the value we're bringing are these novel machine learning algorithms that can solve problems for patients. And none of that is possible without being able to create proprietary datasets that are highly accurate and labeled in an efficient, compliant, and streamlined way. Since the core product we use to power all of this is Label Studio Enterprise, it's the most important technology in our stack.”

“Since the core product we use to power all of this is Label Studio Enterprise, it's the most important technology in our stack.”

“When you're doing these large scale clinical studies, you need expert reviewers to sign off on the underlying annotations. The ability to define the workflow between annotator and reviewer has been huge for us.”

“We didn’t want our DevOps teams running around trying to figure out why we're running out of memory,”

“it accomplished everything we needed to do from a feature/functionality standpoint.”

“At the end of the day, the value we're providing is novel machine learning algorithms that can solve problems for patients. None of that is possible without being able to create proprietary datasets that are highly accurate and labeled in an efficient, compliant, and streamlined way.”

“The partnership with HumanSignal Data Services gives Khan Academy a stronger evidence base for evaluating and improving its moderation systems, so the team can stay focused on helping students learn and supporting its mission to provide a free, world-class education to anyone, anywhere.”

“Some of our most critical external communications globally depend on this work, so HumanSignal's time and attention here is helping keep kids safe.”

“@LabelStudioHQ is amazing. In 10 minutes I have set up a platform where I can label time series anomalies. This is perfect.”

“@LabelStudioHQ is a versatile open-source tool that supports a wide range of annotation tasks, with ML backend capabilities for automation, making it a powerful tool for any machine learning project.”

“[Label Studio] allows a quick learning curve via a user-friendly interface. It happens (very often) in a data science project that annotation is the work of experts in the field, in addition to the data scientist in charge of developing the solution. In this case, an interface that guarantees an optimal user experience becomes necessary.”