ml-engineering

The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!

TensorZero creates a feedback loop for optimizing LLM applications — turning production data into smarter, faster, and cheaper models.

An AI-powered data science team of agents to help you perform common data science tasks 10X faster.

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

A "production-ready" simple project template to quickly start an Artificial Intelligence (AI), Machine Learning (ML) and/or Data Science (DS) project with basic files, branches and directory structure.

Scaffolding for serving ml model APIs using FastAPI

Kafka variant of the MLOps Level 1 stack

MLOps Case Study

Companion notebooks for blogs/tutorials on ML4Devs website.

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

Fast, private data connectors for AI ⚡️🤖

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

Study notes and demos.

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

This Repo contains a Box Detection Application capable of identifying box containers in conveyor belt pictures.

Vehicle data classification (supervised, unsupervised learning)

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.

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.