Notebooks and Code about Generative Ai, LLMs, MLOPS, NLP , CV and Graph databases

Notebooks to learn Databricks Lakehouse Platform

Azure Databricks - Advent of 2020 Blogposts

Deploy and Serve Model using Azure Databricks, MLFlow and Azure ML deployment to ACI or AKS

Laboratoare licență anul IV — Bioinformatica și Genomică Funcțională, cu focus pe ML, rețele și GenAI.

MLFLow Server - Jupyter Notebook Dockerized StarterKit

Modern reactive ML development with Marimo notebooks and MLflow experiment tracking

A meticulously curated collection of hands-on Jupyter notebooks, designed to illuminate the comprehensive application of MLflow across a spectrum ranging from foundational Machine Learning principles to pioneering Generative AI paradigms.

ML detecting credit-card frauds

Some sample notebooks from my evaluation of MLFlow

Example of use of MLFlow tracking functionality in the notebook

End-to-end deep learning project...

The Jupyter Notebook explains basic features of MLFlow experiment tracking using Dagshub

🏦 A credit risk prediction system using RFM-based proxy labels and interpretable ML models. Built with FastAPI, MLflow, Docker, and TypeScript notebooks for model development, tracking, and deployment.

A repo containing a few exploratory notebooks for statistical (ARIMA) and supervised ML (random forest, KNN) approaches to time series analysis of monthly retail sales (sourced from St. Louis Fed). Notebooks also explore the use of MLFlow for experiment tracking, model registration, and deployment/inference.

Vehicle Insurance Domain is 1st MLOPs Production level Application || 2025 || Tools || Python || MongoDB || Notebook || Pipeline || CI-CD || Docker || Git || AWS S3 || FastAPI || MLflow || DVC || More..

A modular MLOps pipeline for end-to-end machine learning: automated data validation, feature engineering, multi-model training, MLflow/DagsHub tracking, and FastAPI deployment. Containerized, extensible, and production-ready for real-world projects. Explore code, docs, and notebooks for reproducible ML workflows.

A practical guide to fine-tuning machine learning models using Databricks notebooks. This repository provides hands-on examples, workflows, and best practices for customizing pre-trained models on Databricks, covering data preparation, training, evaluation, and deployment steps.

A modular tool for creating synthetic datasets to train, test, and prototype AI models. It includes customizable scripts, a Jupyter notebook for data exploration, and support for various AI use cases like supervised learning, data augmentation, and federated learning.

YouTube Comment Sentiment Analysis: Chrome extension and Flask API for analyzing YouTube and Reddit comments using NLP and machine learning. Features a modern UI, color-coded sentiment cards, interactive charts, and video demo. Includes Jupyter notebooks for data science and MLflow experiment tracking.