Products
Scale RapidThe fastest way to production-quality labels.
Scale StudioLabeling infrastructure for your workforce.
Scale 3D Sensor FusionAdvanced annotations for LiDAR + RADAR data.
Scale ImageComprehensive annotations for images.
Scale VideoScalable annotations for video data.
Scale TextSophisticated annotations for text-based data.
Scale AudioAudio Annotation and Speech Annotation for NLP.
Scale MappingThe flexible solution to develop your own maps.
Scale NucleusThe mission control for your data
Scale ValidateCompare and understand your models
Scale LaunchShip and track your models in production
Scale Document AITemplate-free ML document processing
Scale Content UnderstandingManage content for better user experiences
Solutions
Resources
Company
Skip Scooters
Scaling ground-truth data with Scale. We work to create large volumes of training data for their classification models.
the overviewAbout Skip
Skip Scooters provides reliable last-mile transportation by offering shared electric scooters. The company works with cities to ensure fleets are operated with the support of the surrounding communities.
Skip believes in working together with transportation departments to ensure their fleets and riders are complying with local regulations.
Company size: 100
Industry: Last-mile transportation
Product: Image Categorization
Location: San Francisco, CA
the problemEnforcing parking regulations across multiple cities
Each city provides a unique set of regulations they expect Skip’s riders to comply with. The business-critical problem Skip focused on solving is ensuring local parking regulations are met by the riders. They prompt the riders to upload a picture of the parked scooter at the end of every ride. The challenge is analyzing these images in real-time against local parking regulations to determine compliance. Manually analyzing these images is time-consuming and cost-prohibitive in the long run.
“Scale AI proved to be a valuable data annotation partner for our City Operations Team, providing us high quality of training and validation data that helped us significantly improve the accuracy of our image classification models.”
Greg Stewart
Central Operations Project Manage, Skip
the solutionDetermining parking compliance using Image Categorization
Skip decided to automate the enforcement of local parking regulations by creating a machine learning model that can classify images. To train the model, they needed training and validation data annotated according to specific instruction sets. The annotation task would entail classifying whether an image of a parked scooter is in compliance with the local regulations. The instructions vary based on the city, resulting in a complex data annotation project.
Skip looked into various solutions for its data annotation needs, but chose Scale AI for the ease of use, rapid turnaround time, and the ability to classify images based on complex instruction sets. “Scale AI proved to be a valuable data annotation partner for our City Operations Team,” said Greg Stewart, Central Operations Project Manager, “providing us high quality of training and validation data that helped us significantly improve the accuracy of our image classification models.
the futureTraining Data to Bet On
The image classification solution helps Skip to determine parking compliance within seconds, providing riders immediate feedback, resulting in better rider experience. It also significantly reduces the cost of enforcing compliance in the long run.
Get Started Today
