Accelerate the Vision AI lifecycle with Agile ML and real-time automated labeling.
Accelerate the Vision AI lifecycle with Agile ML and real-time automated labeling.
Hasty AI, now part of the CloudFactory ecosystem as of 2026, is a premier Vision AI platform that redefined the manual annotation process through its 'Agile ML' methodology. Unlike traditional platforms that treat labeling and model training as separate silos, Hasty integrates them into a single feedback loop. Its technical architecture utilizes 'Labeling Assistants'—models that learn from your annotations in real-time, providing automated suggestions after just a few manual labels. By 2026, Hasty has evolved into a full-stack Vision AI development environment, offering integrated model architectures, neural search capabilities for data curation, and automated quality control metrics. The platform is specifically designed for teams where data privacy and high-precision labeling (such as medical imaging or satellite analysis) are paramount. Its market position is unique as it combines the software-driven efficiency of a SaaS tool with the optionality of CloudFactory’s managed workforce, ensuring that high-growth startups and enterprises can scale their vision data pipelines without the typical bottleneck of manual data processing.
Accelerate the Vision AI lifecycle with Agile ML and real-time automated labeling.
Quick visual proof for Hasty AI. Helps non-technical users understand the interface faster.
Hasty AI, now part of the CloudFactory ecosystem as of 2026, is a premier Vision AI platform that redefined the manual annotation process through its 'Agile ML' methodology.
Explore all tools that specialize in train machine learning models. This domain focus ensures Hasty AI delivers optimized results for this specific requirement.
Explore all tools that specialize in perform image segmentation. This domain focus ensures Hasty AI delivers optimized results for this specific requirement.
Explore all tools that specialize in object detection. This domain focus ensures Hasty AI delivers optimized results for this specific requirement.
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Algorithms that prioritize the most informative samples for labeling, reducing data requirements by focusing on low-confidence predictions.
Proprietary models that provide instance segmentation and bounding box suggestions that update dynamically as you label.
Uses embeddings to search through millions of images based on visual similarity or specific object features.
Runs a consensus model against human annotators to flag potential errors and outliers.
Full support for medical imaging formats with 16-bit depth handling and windowing tools.
No-code environment to train and test popular architectures like EfficientDet or Mask R-CNN.
Allows for complex nested metadata structures per object, essential for fine-grained classification.
Account creation and project workspace initialization.
Uploading raw image or video datasets via Web UI, Python SDK, or S3/Azure Blob integration.
Defining the label ontology including classes, attributes, and hierarchy.
Manual annotation of the first 10-20 images to initialize the first labeling assistant.
Training the first 'Assistant' model within the Hasty Model Playground.
Deploying automated assistants to suggest labels for the remaining dataset.
Utilizing the 'Neural Search' feature to identify and fix edge cases in data.
Running automated quality control checks to detect labeler inconsistencies.
Exporting validated data in desired ML format or deploying the model to Hasty’s inference engine.
Setting up a continuous learning loop where new production data is fed back into the platform.
All Set
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Verified feedback from other users.
“Users praise the platform for its massive speed gains in labeling and the intuitive integration of model training. Some concern regarding the shift to enterprise-only pricing.”
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