ml-engineering

Notes for Machine Learning Engineering for Production (MLOps) Specialization course by DeepLearning.AI & Andrew Ng

MLOps Case Study

Build end-to-end Machine Learning pipeline to predict accessibility of playgrounds in NYC

Code for "Training models when data doesn't fit in memory" post

Scaffolding for serving ml model APIs using FastAPI

Kafka variant of the MLOps Level 1 stack

An easy-to-use tool for making web service with API from your own Python functions.

The work shown in this repository is part of the Udacity scholarship program in collaboration with Microsoft for Machine Learning Engineer Nanodegree.

In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.

Showcase of MLflow capabilities

🔥🔥🔥🔥🧊🔥🔥 A Data Platform for Monitoring and Detecting Anomalies in Real-Time.

plagiarism detector that examines a text file and performs binary classification; labeling that file as either plagiarized or not, depending on how similar that text file is to a provided source text. Detecting plagiarism is an active area of research; the task is non-trivial and the differences between paraphrased answers and original work are often not so obvious.

A task queue enabling websites to serve ML models -- with RabbitMQ, Celery, all the good stuff.