Tool for automated generation of computational workflows.

This project promulgates an automated end-to-end ML pipeline that trains a biLSTM network for sentiment analysis, experiment tracking, benchmarking by model testing and evaluation, model transitioning to production followed by deployment into cloud instance via CI/CD

Identifies the faulty wafer before it can be used for the fabrication of integrated circuits and, in photovoltaics, to manufacture solar cells. The project retrains itself after every prediction, making it more robust and generalized over time.

An automated pipeline for homology-modeling based on modeller

AutoDataAnalyzer: Automate data ingestion, analysis, and visualization with AI/ML-powered pipelines. Features natural language query processing, interactive Plotly visualizations, and seamless deployment via Docker.

The aim is to detect a fault in a Wafer sensor by looking at the data that is generated by the sensor and then classifying them into Good Wafer (-1) or Faulty/Bad Wafer (1). Used Automated Process for Training and Predictions Process. For the training Process, the Program automatically Selects the Best performing out of 5 different Classificatio…