There are 2 repositories under ml-engineering topic.
TensorZero is an open-source stack for industrial-grade LLM applications. It unifies an LLM gateway, observability, optimization, evaluation, and experimentation.
The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!
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
Ultimate AI research and engineering course
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.
Kafka variant of the MLOps Level 1 stack
Scaffolding for serving ml model APIs using FastAPI
🔥🔥🔥🔥🧊🔥🔥 A Data Platform for Monitoring and Detecting Anomalies in Real-Time.
Companion notebooks for blogs/tutorials on ML4Devs website.
BMAD AI/ML Engineering Expansion Pack - Streamlined framework for AI Singapore programs (MVP, POC, SIP, LADP) with specialized agents, workflows, and templates for ML/LLM development
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
Study notes and demos.
This Repo contains a Box Detection Application capable of identifying box containers in conveyor belt pictures.
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.
Vehicle data classification (supervised, unsupervised learning)
An easy-to-use tool for making web service with API from your own Python functions.
ML Engineering best practices | ML model deployment as a FastAPI service, containerized with Docker for scalability and reproducibility.
ML Engineering best practices | Deploying an ML API with FastAPI, PostgreSQL, and Docker for scalable model storage and inference.
In this repository I have explained different ML Algorithms with their code.
HyperParams: A Decentralized Framework for AI Agent Assessment and Certification
Crack SWE (ML) / DS MAANG Interviews
The work shown in this repository is part of the Udacity scholarship program in collaboration with Microsoft for Machine Learning Engineer Nanodegree.
"When in doubt, use brute force." - Ken Thompson
Showcase of MLflow capabilities
🧠A hands-on workspace for practicing machine learning concepts, data preprocessing, and experimenting with small ML projects. This repo includes foundational Python scripts, real-world mini-projects, and experiments that reflect a progressive learning journey in applied machine learning.
🔒 Leverages Machine Learning and Deep Learning models to identify malicious activities in network traffic, enhancing cybersecurity.
A complete and in-depth machine learning resource containing detailed notes, mathematical explanations, Python code, and Jupyter notebooks., and lectures.
This project fine-tunes a MobileNetV2 model for dog breed classification using labeled image data. It emphasizes strong software engineering practices such as version control, modular code, and continuous integration, ensuring a scalable, maintainable machine learning pipeline with experiment tracking.