Mlops Production Ready Machine Learning Project Github

This project demonstrates a complete MLOps pipeline for a machine learning model using MLflow for experiment tracking, GitHub Actions for CI/CD, and Docker for containerization. It includes modular code for data preprocessing, model training, evaluation, and deployment via FastAPI. Below are some essential tools and platforms that support development, collaboration, and visualization in this project: Anaconda A powerful distribution for Python and data science that simplifies package management and environment handling. Visual Studio Code (VS Code) A lightweight yet powerful code editor with robust support for Python development and extensions. Git Version control system to track changes, collaborate with others, and maintain code history.

If you're serious about working with AI in production — not just reading papers or playing with toy models — this is for you. Shantanu Ladhwe’ve had hands-on experience building end-to-end AI products: from model training to deploying AI agents. Along the way, he curated this 13 GitHub repositories that he personally found practical, insightful, and actually usable in real-world applications. Whether you're starting your ML journey or scaling GenAI agents into production, these repos will fast-track your learning. Hands-on ML from the iconic O’Reilly book (Scikit-Learn, Keras, TensorFlow — 3rd Ed.). Everything is covered from regression to deep nets with practical notebooks.

View Repo The best place to start if you're new to ML. Beginner-friendly Jupyter notebooks with rich visuals and real examples. View Repo In the age of data-driven decision-making, machine learning (ML) has become a cornerstone for businesses across industries. However, deploying ML models and maintaining them in production requires more than just coding skills; it demands a solid understanding of MLOps (Machine Learning Operations).

To help you navigate this crucial field, we've curated a list of 10 GitHub repositories that offer valuable resources, tools, and frameworks to help you master MLOps. In this article, we will explore, 10 GitHub Repositories to Master MLOps. These 10 GitHub repositories offer a diverse range of tools to help you build, scale, and monitor machine-learning models in production environments. Description: This repository hosts a collection of Jupyter notebooks that showcase the various capabilities of Azure Machine Learning. You'll find practical examples of model training, deployment, and MLOps workflows, making it a great starting point for those interested in Azure's ecosystem. Link: https://github.com/Azure/MachineLearningNotebooks

Description: This repository provides a practical implementation of MLOps using Python and Azure. It covers the entire ML lifecycle—from data preparation to deployment and monitoring—making it an excellent resource for hands-on learning. Begin your MLOps journey with these comprehensive free resources available on GitHub. It is becoming more important to master MLOps (Machine Learning Operations) for those who want to effectively deploy, monitor, and maintain their ML models in production. MLOps is a set of practices that aims to merge ML system development (Dev) and ML system operation (Ops). Luckily, the open-source community has created numerous resources to assist beginners in mastering these concepts and tools.

Here are ten GitHub repositories that are essential for anyone looking to master MLOps: It is a 9-week study plan designed to help you master various concepts and tools related to Model Monitoring, Configurations, Data Versioning, Model Packaging, Docker, GitHub Actions, and AWS Cloud. You will learn how to build an end-to-end MLOps project, and each week will focus on a specific topic to help you achieve this goal. The repository provides MLOps end-to-end examples & solutions. A collection of examples showing different end to end scenarios operationalizing ML workflows with Azure Machine Learning, integrated with GitHub and other Azure services such as Data Factory and DevOps. GitHub repositories provide hands-on learning of real-world MLOps workflows.

Tools like MLflow, Kubeflow, and DVC show how scaling and tracking work in practice. Beginner-friendly repos make it easier to move from AI experiments to deployment. Machine Learning Operations (MLOps) has developed into an important space in the world of AI. Building a model within a notebook is just the first step; the trick is making sure that the model works in the real world. MLOps is essential for helping shift machine learning projects from a proof-of-concept pace to production. GitHub is still one of the best ways to gain these understanding of MLOps.

There are many open-source repositories. Github is a great place to find where developers and organizations will share code, tools, and practical examples. Here are ten GitHub repositories that learners can benefit from concerning MLOps in practice. Youtube Playlist: https://youtube.com/playlist?list=PLkz_y24mlSJZvJOj1UXiJPVRQrNFdNEXX&si=FRFLpnve9MS6Rii9 Machine Learning Engineering (MLE) involves applying scientific principles, tools, and techniques from machine learning and traditional software engineering to design and build complex computing systems. The Production ML focuses on the practical aspects of deploying machine learning models into production environments.

It covers the challenges and best practices involved in building, testing, deploying, and monitoring ML systems. MLOps emphasizes the importance of standardizing processes and technology capabilities to enable rapid and scalable deployment and operation of ML systems. Technical Debt refers to accumulated problems in software code or architecture that arise from neglecting software quality during development, resulting in additional future work and costs. ML systems have additional "opportunities" to accumulate technical debt, and ML Engineers often face these challenges primarily. Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.

Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform. End to end machine leanring project: This repository serves as a simplified guide to help you grasp the fundamentals of MLOps. The project is a concoction of research (audio signal processing, keyword spotting, ASR), development (audio data processing, deep neural network training, evaluation) and deployment (building model artifacts, web app development, docker, cloud PaaS) by... An end-to-end MLOps pipeline(CI/CD/CT/CM) project for training, versioning, deploying, and monitoring machine learning models using FastAPI, Kubernetes, MLflow, DVC, Prometheus, and Grafana. Machine Learning Engineering (MLE) involves applying scientific principles, tools, and techniques from machine learning and traditional software engineering to design and build complex computing systems.

The Production ML focuses on the practical aspects of deploying machine learning models into production environments. It covers the challenges and best practices involved in building, testing, deploying, and monitoring ML systems. MLOps emphasizes the importance of standardizing processes and technology capabilities to enable rapid and scalable deployment and operation of ML systems.

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