ML Tracker

The ML Tracker is a web-based platform designed to simplify and enhance the management of machine learning workflows. It addresses key challenges in ML projects, such as data and model versioning, experiment tracking, and performance visualization.

Goal

Provide an integrated, user-friendly web platform for managing machine learning workflows, enhancing operational efficiency, and addressing challenges in data and model versioning, experiment tracking, and visualization.

Approach

Created a dashboard for tracking and comparing experiments. Designed a backend for managing model metadata and datasets. Integrated visualization tools for metrics comparison.

Features

•Data and Model Versioning: Track and manage different dataset and model versions to ensure reproducibility and reduce errors.
•Experiment Tracking: Record experiments, configurations, and results for streamlined testing and evaluation.
•Advanced Visualization: Enable effective visualization of metrics and model performance for informed decision-making.
•MLOps Integration: Automate CI/CD pipelines for deployment, monitoring, and governance of ML models.
•End-to-End Workflow Management: Seamless handling of the entire ML lifecycle, improving collaboration and reducing inefficiencies.
•Ease of Use: Intuitive interface to reduce complexity, enhance adoption, and eliminate workflow fragmentation.

README Not Found

The README file for this project could not be loaded.

View on GitHub

Tech Stack

JavaScriptPythonExpressNode.jsMongoDB