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Data Annotator for Machine Learning (DAML) is an application that helps machine learning teams facilitating the creation and management of annotations.

Core features include:

• Support for common annotation tasks:
  • Text classification
  • Named entity recognition
  • Tabular classification and regresion
  • Images recognition with bounding boxes and polygons
  • Log labeling
  • Question answer
• Active learning with uncertainly sampling to query unlabeled data
• Project tracking with real time data aggregation and review process
• User management panel with role-based access control
• Data management
  • Import in common data formats
  • Export in ML friendly formats
  • Data sharing through community datasets
• Swagger API for programmatic labeling, connecting to data pipelines and more

• ATO 2021 talk
• DAML medium article
• User guide
• Tutorial
• DAML release note

DAML project includes three components:

• annotation-app: Angular application for the UI
• annotation-service: Backend services built with Node & Express
• active-learning-service: Django application providing active learning api using modAL library for pool-based uncertainty sampling to rank the unlabelled data

• For the docker version usage to seerun with docker documentation
• For development environment and build configuration seebuild documentation
• For the slack integration configuration seemanifest documentation

DAML project team welcomes contributions from the community. For more detailed information, seeCONTRIBUTING.md.

Have a bug or a feature request? Please first read the issue guidelines and search for existing and closed issues. If your problem or idea is not addressed yet, please open a new issue.

Copyright 2019-2021 VMware, Inc.SPDX-License-Identifier: Apache-2.0.