[Book-2021] Practical MLOps O'Reilly Book

Hands-on MLOps projects to explore and learn the practical aspects of machine learning engineering for production.

This repository provides a comprehensive ML infrastructure for CTR prediction, focusing on AWS services and offering practical learning experience for MLOps.

MLOpsCommunity's reading group for _Practical MLOps_ by Noah Gift and Alfredo Deza

An end-to-end predictive maintenance project built on the NASA CMAPSS turbofan dataset. Includes data preprocessing, feature engineering, model training, evaluation, and a production-ready FastAPI inference service. Designed to demonstrate practical ML and MLOps skills through a real, working workflow.

📚 O'Reilly 『MLOps 실전 가이드』 소스코드 길라잡이

[📚💻] Notes, code examples and exercises based on the book Practical MLOps - Operationalizing Machine Learning Models by Noah Gift & Alfredo Deza

In this project, we build a practical MLOps project that demonstrates a local feature store with Parquet file offline feature store and Redis online feature store to serve features for the training of a Machine Learning model and for Inferencing

In this project, we give a practical, end-to-end MLOps project that detects data / concept drift, exports drift metrics to Prometheus, visualizes & alerts in Grafana, Alertmanager, and Slack.

Practical in depth hands-on MLOPs utilising best tools, practice and strategies

Notes and Exercises related to book : Practical MLOps By Noah Gift, Alfredo Deza

Practical Hands on Exercises for MLOps

MLOps Zoomcamp Objective: Teach practical aspects of productionizing ML services — from training and experimenting to model deployment and monitoring.

A comprehensive guide to ML model development with an MLOps mindset - covering distributed processing, pipeline orchestration, and production-ready model design with practical examples.

A Jupyter Notebook collection designed to develop a practical understanding of Machine Learning Operations (MLOps) defined in the NESA Software Engineering Course Specifications pg 27.

Forecasting Vehicle Sales Using XGBoost – A Practical Guide to MLops Best Practices

Dépôt d’expérimentations et d’exercices basés sur le livre Practical MLOps: Operationalizing Machine Learning Models de Noah Gift et Alfredo Deza. L’objectif est de mettre en pratique les concepts d’ingénierie MLOps à travers des exemples concrets, du prototypage au déploiement de modèles en production.

A practical implementation of MLOps process.

This repository contains all practical materials and projects for someone to learn Mlops

AI_ML — A collection of practical projects exploring Machine Learning, LLMs, and MLOps through hands-on experimentation and real-world implementation.

A collection of practical projects, tools, and experiments exploring Large Language Models (LLMs), including fine-tuning, prompt engineering, evaluation, and deployment using modern MLOps workflows.

A curated collection of high-quality learning materials, research papers, datasets, frameworks, and practical guides covering every major area of Artificial Intelligence — including Machine Learning, Deep Learning, NLP, Computer Vision, Generative AI, Reinforcement Learning, and MLOps.

Slides and demo code from my presentation entitled Practical MLOps with GitHub and Azure ML

"Practical MLOps" Book Exercises, Tests and Practice solved by Enric Domingo while reading the book

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Practical code & infrastructure as code snippets to get various mlops tasks done.

Repository with exercises from Chapter 1 of Practical MLOps. Python application practices basic MLOps, using Makefile, GitHub Actions, Docker, AWS ECR, CodeBuild and Terraform.

The book's exerices and my notes

MLOps Zero to Hero — practical tutorials and projects to learn the MLOps lifecycle from scratch.

A collection of machine learning projects and experiments covering end-to-end ML pipelines, from model development to deployment. Focused on practical AI engineering, ML frameworks and MLOps practices.