Using machine learning to solve complex business problems requires massive volumes of training data that is accurately labeled and annotated in the context of the problem space. Maintaining that quality at scale traditionally has been a challenge, especially with sophisticated enterprise use cases.
A Platform Built to Scale
The Alegion platform is designed to deliver production-grade data volume and quality. Advanced ML capabilities like conditional logic, multi-stage workflows, and quality control routing accelerate data annotation, reserving human oversight for subjective and complex judgments—where it matters the most.
Scale Every Form of Data Labeling and Annotation
Data labeling is a critical contributor to model performance, but it’s a major blocker in supervised learning and your labeling requirements change as you move from proof of concept to production.
Whether it’s segmenting images, extracting inferences from a transcript, or identifying overlapping objects in a video with non-linear movements and categorizing them into a hundred classes, our platform has handled it.
Breaking up complex jobs into micro-tasks, implementing conditional logic, and iterating on workflow designs allows the platform to accommodate even the most challenging tasks.
Purpose-built Annotation Tools
Our suite of visual annotation tools includes bounding boxes, key points, polygons, and splines, as well as text-based classification functions for NLP and named entity recognition.
Because our toolset can be precisely configured to handle your unique use case, you get higher quality data and higher throughput than with off-the-shelf or in-house labeling tools.
Adaptive Quality Controls
A one-size-fits-all approach to quality doesn’t produce high precision, recall, and F1 scores across all projects. We can dial in the balance of human and ML oversight for each task or escalate low-confidence judgments to an administrative review, enabling the platform to actively learn over time.
Our platform also supports best-practice quality control methodologies like gold data scoring, judgement consensus, and domain-expert reviews.
The Alegion API automates data pipeline management in enterprise ML environments and provides do-it-yourself platform access to data scientists and ML engineers.
Our API integrates with third-party platforms to automate pre- and post-processing, and supports batch processing to reduce error and enable continuous data exchange throughout the model development lifecycle.
Training the annotators to understand your definition of accuracy is a critical piece of quality management. Alegion handles the skills screening and job-specific training, so that you get high quality labeling without having to manage the workforce.
Our platform assigns labeling tasks to annotators according to their skill, profile, and ability to meet customer security requirements.
Alegion’s platform has built-in controls around data protection, infrastructure security, and user access. The platform ensures that your data is accessible only by pre-qualified teams via signed URLs.
Whether requirements mandate an NDA workforce, geographical isolation, or HIPAA/PCI provisions, our platform serves customers with sensitive data in government, medical, defense contracting, or financial institutions.
Platform Support and Expertise
Our customers can elect to offload management tasks to their dedicated solutions specialist, whose goal is to understand your project goals and make sure you get the most out of our platform.
Platform configuration, workforce training, and quality monitoring? Task testing and iteration? Feedback to help refine your target values? Slacking with you while Netflix binging with the family? Yes, we do all of those things.
Most labeling solutions require you to work around their existing specifications. Our platform and workflows can be configured for your unique labeling requirements.
ML and Human Powered
Algorithms enhance human accuracy and reduce cognitive load by providing initial judgements and routing tasks to optimal workflows when appropriate.
High Confidence Results
Iterative testing and optimizations ensure our platform delivers high accuracy data that drives increased model confidence. And you have full visibility into our QC process.
Images, Text, Video, and Audio
Data Labeling and Classification
Episode 4: Bias in ML
In this podcast episode, we discuss helpful mathematical bias and examples ...Read More
5 Key takeaways from Trade show season
Trade show season has begun and these conferences continually demonstrate t...Read More
Machine Learning for the Enterprise
This emerging world of AI runs on data. Learn how to properly prepare your ...Read More