The Alegion approach
Machine learning project expertise is in short supply. We are ready to support you throughout the lifecycle of your project. We assign an Alegion solutions specialist to every customer engagement. Our specialist understands your project goals, and applies proven methodologies and best practices to deliver your solution through our platform.
HUMAN & ML-DRIVEN
Alegion built its platform to scale data labeling, so that data scientists can mine for value in their data without losing hours to labeling tasks.
How It Works
Unlike other data labeling solutions, Alegion does not require you to spend hours designing the labeling tasks and managing the annotators, or waste cycles trying to improve your data accuracy. Bring us your data and we can take care of everything else.
How we accelerate data labeling
Data connectivity is straightforward. The Alegion platform can securely connect to your object store in all major cloud providers or data can be transferred via API or file-sharing services. We support all popular data export formats and can tailor the output to your needs.
Our platform selects pre-screened annotators who are best qualified to handle your tasks. We rigorously train those annotators on your use case and your tasks to make sure they understand your definition of accuracy. We can handle it all, or you can opt to bring your own workforce.
Task Design & Distribution
With our platform, there is no need for you to design, code, and test your annotation tasks and workflows. Your dedicated solutions specialist will configure the annotation tooling and workflow (e.g. create microtasks, multi-stage workflows, conditional logic), run small batch testing to validate output quality with you, then distribute the tasks at scale.
After the tasks are configured and the workforce is trained, work is distributed en masse to the annotators. Throughout your project, your solutions specialist oversees the entire process, auditing samples of the labeled data with you to validate the accuracy and improve data quality and throughput over time.
Some projects reach target accuracy with two annotation passes and a tie-breaker or answers validated against gold data. Others require a more complex review and escalation workflow with verification from a subject matter expert. This is where we collaborate with you to build a QA strategy, validate the approach, and continuously optimize labeling accuracy.
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