Production Ready Github Repos To Master Ml Mlops Llms Rag Ai

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 🌟 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 I help people build $10K/mo+ AI Agencies, starting from zero | Scaled mine to $30k+ | Founder at MMWA Skool | ex-Amazon 12 GitHub repos the top 1% of AI Engineers use daily.

(ML ▸ RAG ▸ PyTorch ▸ MLOps ▸ Agents) Save this list 👇 1. Machine Learning for Beginners — Microsoft Step-by-step Jupyter notebooks that make ML concepts visual & beginner-friendly. 🔗 https://lnkd.in/deCYJehp 2. Learn PyTorch for Deep Learning Great intro to PyTorch. Slightly dated, but fundamentals still rock solid. 🔗 https://lnkd.in/dpT76mqP 3.

Hands-On Large Language Models Companion repo to the Hands-On LLM book. Covers everything from basics to finetuning. 🔗 https://lnkd.in/drVcU5d4 4. AI Agents for Beginners Free 11-lesson course — no fluff, just code. Best way to start with agents. 🔗 https://lnkd.in/d4gdmUwh 5.

Prompt Engineering Guide Your all-in-one hub for prompts: guides, papers, lectures, curated examples. 🔗 https://lnkd.in/dcJ4V9Kk 6. LLM Course Hands-on lifecycle: design → training → deployment. Includes roadmaps + Colabs. 🔗 https://lnkd.in/dWd5QQyg 7. GenAI Agents Practical tutorials for tool-using agents & advanced workflows.

🔗 https://lnkd.in/dzwW_hVA 8. RAG Techniques One of the most complete collections of Retrieval-Augmented Generation tutorials. 🔗 https://lnkd.in/dUKW7MuJ 9. Agents Towards Production Hands-on guide to every building block of a GenAI agent stack. 🔗 https://lnkd.in/d_9-fR8P 10. Awesome Generative AI A massive curated list: tools, repos, papers, and more.

Perfect for R&D. 🔗 https://lnkd.in/d336fXPU 11. Made With ML Covers the ML product lifecycle: design → CI/CD → monitoring. Production-ready gold. 🔗 https://lnkd.in/d7HCaeqj 12. Designing Machine Learning Systems Code + diagrams + summaries from the classic O’Reilly book.

🔗 https://lnkd.in/dTwThYhV And if you want more material on AI Agents, here: 👉 rebrand.ly/levaai ♻️ Repost to help your network. And follow Giovanni Beggiato for more. I build AI systems and automations for businesses and help engineers do the same | 3,000+ engineer community. Nice list. I think the RAG techniques and AI agents repos are super helpful for what's happening now in the field. Executive Director - Enterprise GenAI Architect @ Wells Fargo | Intelligent Process automation Expert | AI Strategist | AI Ethics and Governance | Process Transformation and Optimization

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. Master LLMs through books, courses, tutorials, exercises, projects, and comprehensive guides that cover everything from foundational concepts to advanced techniques. If you are not familiar with large language models (LLMs) today, you may already be falling behind in the AI revolution.

Companies are increasingly integrating LLM-based applications into their workflows. As a result, there is a high demand for LLM engineers and operations engineers who can train, fine-tune, evaluate, and deploy these language models into production. In this article, we will review 10 GitHub repositories that will help you master the tools, skills, frameworks, and theories necessary for working with large language models. This repository is a goldmine for learning prompt engineering, one of the most critical skills for working effectively with LLMs. It provides tips, tricks, and examples to help you craft better prompts and get the most out of models like GPT-4o. This repository offers a comprehensive course on LLMs, designed for learners of all levels.

It includes tutorials, projects, and hands-on exercises to help you understand and apply LLMs effectively. RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more. Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.

AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots. 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. Data Scientist | Machine Learning Engineer | Python | Geophysics 𝐌𝐋 𝐌𝐋𝐎𝐏𝐬 𝐋𝐋𝐌 𝐑𝐀𝐆 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐇𝐚𝐧𝐝𝐬-𝐨𝐧 𝐒𝐨𝐮𝐫𝐜𝐞𝐬 When I first started exploring advanced AI topics like LLMs, RAG pipelines, and AI agents, it felt overwhelming. Too many resources, not enough practical guidance. The turning point for me was focusing on 𝐡𝐚𝐧𝐝𝐬-𝐨𝐧 𝐫𝐞𝐩𝐨𝐬𝐢𝐭𝐨𝐫𝐢𝐞𝐬 that bridge theory with real-world application. That’s why I curated this list of 13 high-quality GitHub repos across 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐌𝐋𝐎𝐩𝐬, 𝐋𝐋𝐌𝐬, 𝐑𝐀𝐆, 𝐚𝐧𝐝 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬.

These are not random picks; they come from real production experience. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮’𝐥𝐥 𝐟𝐢𝐧𝐝: • 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Notebooks and beginner-friendly resources to solidify fundamentals. • 𝐌𝐋𝐎𝐩𝐬: From design principles to full CI/CD and monitoring workflows. • 𝐋𝐋𝐌𝐬: Courses and practical guides for fine-tuning and deployment. • 𝐑𝐀𝐆: Comprehensive tutorials for building retrieval-augmented systems. • 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬: From basic concepts to advanced agent-based architectures.

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