An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈

Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.

A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data 🚀

OpenLLM Monitor is a plug-and-play, real-time observability dashboard for monitoring and debugging LLM API calls across OpenAI, Ollama, OpenRouter, and more. Tracks tokens, latency, cost, retries, and lets you replay prompts — fully open-source and self-hostable.

A multi-user Claude Code CLI based Coworker with Multi Agent Orchestrator, Multi model support including Ollama models, Advanced Functional skills, Channel integration for Chat and text messaging platform running completely in docker containers with an Admin module for complete visibility and observability

Python Open-source package that ensures robust and reliable ML models deployments

This repository comprises of the projects and assignments that i have completed during my tenure at Great Lakes for the course program PGP-AIML. This repository also includes the lab work thatwas done during the classes and even those that were given as assessments.

In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on experience building, validating, and deploying machine learning models. You will learn how to build and derive insights from these models using Python, and Azure Notebooks.

Tracking State-of-the-Art AI Models and Performance is an open-source dataset documenting AI advancements from the 1950s to today. It includes model details, organizations, compute requirements, and benchmarks. Researchers and developers can analyze trends, compare models, and contribute updates. The dataset is open for collaboration $ fostering AI

Agentic Workflow Evaluation: Text Summarization Agent. This project includes an AI agent evaluation workflow using a text summarization model with OpenAI API and Transformers library. It follows an iterative approach: generate summaries, analyze metrics, adjust parameters, and retest to refine AI agents for accuracy, readability, and performance.

predict the rent price of a house based on its surface

A streamlined and easy-to-use AI performance evaluation / summary template with modern UI in HTML, including correct percentage chart and comparison with other models, precision, recall, F1-score, and confusion matrix. Enables you to create the result chart within 3 minutes.

Projects on Machine Learning Model using Python

Built a Deep learning Model for Sequential Sentence Classification, for Converting “Harder to Read” text into “Easier to Read ” text.

Machine learning classification project to predict mobile phone price categories based on hardware specifications using Random Forest.

Source code for Cyber-Attack Monitoring and Detection using Machine Learning Techniques paper

Yellowbrick is an useful machine learning visualization library for visualizing model performance. This Jupyter notebook gives an example for using yellowbrick to visualize model performance of a ternary classification task.

SWEETGUARD 🛡🔍 – A data-driven diabetes risk assessment tool that leverages machine learning and public health datasets to predict individualized diabetes risk scores. Using Python 🐍, Power BI 📊, and statistical analysis, this project identifies key lifestyle factors and empowers individuals with personalized health insights.

Neural Networks and Deep Learning Models

ML monitoring service with data drift, model performance metrics, alerts, dashboard, PostgreSQL, Docker, and CI/CD.