HashSlap-Summer-of-Code/ml-core

Design a Unified ML Experiment Tracking Framework

Open

#7 opened on Jun 18, 2025

View on GitHubContributor guide
 (0 comments) (0 reactions) (0 assignees)Jupyter Notebook (11 forks)auto 404
Intermediateenhancementgood first issuehacktoberfesthssoc

Repository metrics

Stars
 (3 stars)
PR merge metrics
 (PR metrics pending)

Description

Description:
Develop a lightweight and consistent way to log and visualize machine learning experiment results across all subfolders (e.g., neural-networks, supervised-learning, anomaly-detection). This helps contributors compare results over time and improve reproducibility.

Expected Tasks:

  • Create a Python utility (e.g., experiment_logger.py) that logs metrics like accuracy, loss, and hyperparameters to a .csv file.
  • Add basic plotting functionality using matplotlib or seaborn.
  • Place the utility in a new folder like utils/ or tools/.
  • Create a sample log for an existing implementation (e.g., Perceptron).
  • Write a README.md in the root directory explaining:
    • How to use the logger.
    • Required libraries.
    • How to integrate it into a new or existing ML script.

Stretch Goal:

  • Explore integration with lightweight experiment trackers like MLflow or Weights & Biases, while keeping setup minimal.

Contributor guide

Research direction
Create a Python utility for logging ML metrics to CSV, add plotting with matplotlib, place in utils/, write README explaining usage. For stretch goal, explore MLflow integration.
Tech stack
python
Domain
machine learning
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonGit
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
75