cloudfactory.com

Hasty (now part of CloudFactory AI Data Platform) is a powerful computer vision annotation and model development tool that has been integrated as a core component of CloudFactory’s AI Data Platform. While Hasty is no longer a standalone tool, its robust annotation capabilities continue to enhance AI-driven workflows across data labeling, model training, validation, and deployment. This description provides an overview, typical use cases, and how to leverage Hasty within the CloudFactory platform.

---

Overview

Hasty serves as an advanced annotation engine within CloudFactory’s AI Data Platform, enabling precise labeling for computer vision tasks such as object detection, segmentation, bounding boxes, polygons, and pixel-accurate masks. It supports scalable, collaborative labeling workflows with built-in quality assurance, auditing, and versioning, helping organizations improve data quality for vision models.

---

How to Use Hasty within the CloudFactory AI Data Platform

1. Access the AI Data Platform: Navigate to the CloudFactory AI Data Platform and sign in with your credentials.
2. Create or select a project: Choose an existing project or create a new annotation project aligned with your model goals.
3. Import data: Upload images, videos, or geospatial assets depending on your use case (e.g., aerial, automotive, retail imagery).
4. Configure annotation tasks: Define labeling schemas (bounding boxes, polygons, masks, keypoints, etc.), validation rules, and worker guidelines.
5. Assign or invite annotators: Add teammates or outsource labeling to a managed workforce. Set quotas, reviews, and SLA targets.
6. Annotate: Workers label assets following the schema. Use the platform’s tools to draw shapes, assign labels, and adjust boundaries as needed.
7. Quality assurance: Apply built-in QA checks, consensus voting, and review workflows to ensure data accuracy.
8. Review and approve annotations: Validate work, resolve disagreements, and approve finalized labels.
9. Export data: Retrieve labeled data in your preferred format (COCO, VOC, YOLO, or custom schemas) for model training.
10. Model integration: Feed the labeled dataset into your GenAI or ML workflows for training, evaluation, and deployment within the CloudFactory platform.

Tip: Use the platform’s automation features to streamline repetitive labeling tasks, enforce schema consistency, and monitor labeling throughput.

---

Use Cases

• Aerial and Geospatial: object detection, land-cover segmentation, and feature extraction from satellite imagery.
• Autonomous Vehicles: lane marking, obstacle detection, and scene understanding annotations.
• Finance/Insurance: document analysis and OCR-like labeling for visual data.
• Retail: product localization, shelf detection, and packaging recognition.

---

Safety and Compliance

• Follow data privacy and access control policies defined in your CloudFactory workspace.
• Ensure team members have appropriate training and adhere to labeling guidelines to maintain data integrity.

---

Core Features

• Centralized annotation engine within the CloudFactory AI Data Platform
• Supports CV labeling types: bounding boxes, polygons, masks, keypoints, and more
• Scalable, collaborative labeling with built-in QA and versioning
• Rich task configuration: labeling schemas, validation rules, and worker guidelines
• Seamless integration with model training pipelines in the platform
• Export-ready formats (COCO, VOC, YOLO, custom schemas)
• Workflow automation and SLA tracking for labeling projects
• Audit trails and data governance for compliance

---

What You Get with Hasty within the Platform

• Enhanced data quality for computer vision models
• Optimized labeling throughput through collaborative workflows
• Tight integration with downstream AI tooling (training, evaluation, deployment)
• Comprehensive project management and reporting

---

Related Resources

• Data Labeling Guides and Best Practices within CloudFactory
• GenAI Model Oversight and Monitoring capabilities
• Pricing, Use Cases, and Webinars on the CloudFactory site

---

How It Works (Summary)

• Import data → Define labeling schema → Assign tasks → Annotate with CV tools → QA/Review → Export → Use in model training
• The platform emphasizes governance, traceability, and scalable annotation to support robust ML pipelines