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1. doordash-duration-prediction doordash-duration-prediction Public

   An end-to-end machine learning project predicting DoorDash delivery durations, utilizing MLOps principles and best practices.

   Jupyter Notebook 1

2. mlops-homework-03 mlops-homework-03 Public

   This project uses Mage to build a pipeline for ingesting, transforming NYC taxi data, and training a linear regression model. The model and artifact (dict vectorizer) are logged and registered wit…

   Python

3. mlops-homework-04 mlops-homework-04 Public

   This Python script predicts NYC taxi trip durations using a pre-trained scikit-learn model, processes data for a specified year and month, and saves the results to an S3 bucket in Parquet format, a…

   Jupyter Notebook

4. mlops-homework-05 mlops-homework-05 Public

   This project builds an MLOps pipeline using Evidently for monitoring model performance and Prefect for task orchestration. It processes NYC taxi data, stores metrics in PostgreSQL, and visualizes r…

   Jupyter Notebook 1

5. llm-homework-04 llm-homework-04 Public

   This Jupyter notebook project evaluates the accuracy of language model responses to generated questions by comparing them to a ground truth dataset using cosine similarity and ROUGE metrics.

   Jupyter Notebook

6. llm-homework-05 llm-homework-05 Public

   This project implements a Retrieval-Augmented Generation (RAG) pipeline using Mage to manage large language models (LLMs) on an hourly schedule.

   Python