Github Umitkacar Awesome Mlops Production Grade Mlops Model

🌟 A comprehensive, production-ready MLOps repository featuring cutting-edge tools, frameworks, and best practices for 2024-2025 🌟 🎯 Explore Tools · 🔥 Get Started · 📚 Documentation · 🤝 Contributing MLOps Ecosystem 2024-2025 is your ultimate guide to building production-grade machine learning systems. This repository curates the most powerful, trending, and battle-tested tools across the entire ML lifecycle - from data annotation to model deployment and monitoring. The hottest trend in ML - Build, deploy, and scale Large Language Models High-quality data is the foundation of great ML models

There was an error while loading. Please reload this page. You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs. A curated list of resources, tools, frameworks, articles, and projects related to Machine Learning Operations (MLOps). Welcome to Awesome MLOps!

This repository aims to gather the best resources related to MLOps, covering a wide range of topics including best practices, tools, frameworks, articles, and projects in the field of Machine Learning Operations. Contributions are welcome! If you have resources, tools, frameworks, articles, or projects related to MLOps that you'd like to add, please open a pull request. A curated list of tools, frameworks, platforms, and resources for Machine Learning Operations (MLOps). MLOps stands at the intersection of machine learning, DevOps, and data engineering. This list is intended for ML engineers, data scientists, DevOps practitioners, and anyone building, deploying, monitoring, and scaling machine learning systems.

The complete MLOps lifecycle showing data processing, modeling, and deployment phases This guide explores machine learning model development through an MLOps lens, focusing on building models that not only achieve high accuracy but also perform efficiently in production environments. We'll cover distributed data processing, pipeline orchestration, and model development fundamentals with practical examples and best practices. Example of Spark DataFrame operations and MLlib pipeline Apache Spark provides a powerful framework for distributed data processing, essential for handling large-scale ML datasets. Key concepts include:

Performance comparison: When to use Spark vs Pandas for different data sizes Learn how to combine machine learning with software engineering to design, develop, deploy and iterate on production-grade ML applications. In this course, we'll go from experimentation (design + development) to production (deployment + iteration). We'll do this iteratively by motivating the components that will enable us to build a reliable production system. Machine learning is not a separate industry, instead, it's a powerful way of thinking about data that's not reserved for any one type of person. Be sure to go through the course for a much more detailed walkthrough of the content on this repository.

We will have instructions for both local laptop and Anyscale clusters for the sections below, so be sure to toggle the ► dropdown based on what you're using (Anyscale instructions will be toggled on... If you do want to run this course with Anyscale, where we'll provide the structure, compute (GPUs) and community to learn everything in one day, join our next upcoming live cohort → sign up... We'll start by setting up our cluster with the environment and compute configurations. Learn how to combine machine learning with software engineering to design, develop, deploy and iterate on production-grade ML applications. In this course, we'll go from experimentation (model design + development) to production (model deployment + iteration). We'll do this iteratively by motivating the components that will enable us to build a reliable production system.

Machine learning is not a separate industry, instead, it's a powerful way of thinking about data that's not reserved for any one type of person. Be sure to go through the course for a much more detailed walkthrough of the content on this repository. We will have instructions for both local laptop and Anyscale clusters for the sections below, so be sure to toggle the ► dropdown based on what you're using (Anyscale instructions will be toggled on... If you do want to run this course with Anyscale, where we'll provide the structure, compute (GPUs) and community to learn everything in one weekend, join our next upcoming live cohort → sign up... We'll start by setting up our cluster with the environment and compute configurations. 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. Instantly share code, notes, and snippets.

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.

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