Skip to main content

Introduction

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

Acharya'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. Acharya 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, Acharya 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

Dependencies​

note

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