Introduction

CleanML is an MLOps tool for data centric AI. CleanML has been built to help a ML team to manage the lifecycle of their Named Entity Recognition projects.

CleanML's features include:

• Curate and annotate data
• Edit data and add custom data
• Present insights about the annotated data
• Export annotated data in multiple data formats
• Show annotated entities distribution on both training data and the data used for evaluating trained algorithms (evaluation data or test data)
• Display suggestions and previous classifications
• Auto-label data with the best trained algorithm on that data
• Configure a custom api/web based dictionary
• Experiment with multiple algorithms with multiple libraries
• Connect an algorithm directly with the code's git repository
• Train off from a custom branch making experimentation easy and integratable into the development cycle
• Train on a remote docker container and on either the CPU or the GPU*
• Write algorithms independent of data format. CleanML will convert to a supported data format before the data is sent for training
• Track data changes and code changes between experiments
• Compare two different trainings of the same algorithm, CleanML tracks data changes which increases debug ability
• Track the progress of data annotation
• Track data overfitting or underfitting of specific entities based on reports from periodic training and data classification
• Compare model deployed in production with a freshly trained model
• Tag and upload data hit in production and once annotated analyze the accuracy of the model deployed in production

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Anything unclear or buggy in this doc, or you couldn't find what you were looking for? Please file an issue on GitHub!