🔥 Trending Repository: vllm

📝 Description: A high-throughput and memory-efficient inference and serving engine for LLMs

🔗 Repository URL: https://github.com/vllm-project/vllm

🌐 Website: https://docs.vllm.ai

📖 Readme: https://github.com/vllm-project/vllm#readme

📊 Statistics:
🌟 Stars: 55.5K stars
👀 Watchers: 428
🍴 Forks: 9.4K forks

💻 Programming Languages: Python - Cuda - C++ - Shell - C - CMake

🏷️ Related Topics:

#amd #cuda #inference #pytorch #transformer #llama #gpt #rocm #model_serving #tpu #hpu #mlops #xpu #llm #inferentia #llmops #llm_serving #qwen #deepseek #trainium

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🧠 By: https://t.me/DataScienceM

🤖🧠 MLOps Basics: A Complete Guide to Building, Deploying and Monitoring Machine Learning Models

🗓️ 30 Oct 2025
📚 AI News & Trends

Machine Learning models are powerful but building them is only half the story. The true challenge lies in deploying, scaling and maintaining these models in production environments – a process that requires collaboration between data scientists, developers and operations teams. This is where MLOps (Machine Learning Operations) comes in. MLOps combines the principles of DevOps ...

#MLOps #MachineLearning #DevOps #ModelDeployment #DataScience #ProductionAI

📌 How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2025-11-12 | ⏱️ Read time: 8 min read

This final part of the series on RAG pipeline evaluation explores advanced metrics for assessing retrieval quality. Learn how to use Discounted Cumulative Gain (DCG@k) and Normalized Discounted Cumulative Gain (NDCG@k) to measure the relevance and ranking of retrieved documents, moving beyond simpler metrics for a more nuanced understanding of your system's performance.

#RAG #EvaluationMetrics #LLM #InformationRetrieval #MLOps

📌 Organizing Code, Experiments, and Research for Kaggle Competitions

🗂 Category: PROJECT MANAGEMENT

🕒 Date: 2025-11-13 | ⏱️ Read time: 21 min read

Winning a Kaggle medal requires a disciplined approach, not just a great model. This guide shares essential lessons and tips from a medalist on effectively organizing your code, tracking experiments, and structuring your research. Learn how to streamline your competitive data science workflow, avoid common pitfalls, and improve your chances of success.

#Kaggle #DataScience #MachineLearning #MLOps

📌 LLM-as-a-Judge: What It Is, Why It Works, and How to Use It to Evaluate AI Models

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2025-11-24 | ⏱️ Read time: 9 min read

Explore the 'LLM-as-a-Judge' framework, a novel approach for evaluating AI systems. This guide explains how to use large language models as automated judges to assess model performance and ensure AI quality control. It provides a step-by-step breakdown of the methodology, explores the reasons behind its effectiveness, and shows you how to implement this powerful evaluation technique.

#AIEvaluation #LLM #MLOps #LLMasJudge

📌 Ten Lessons of Building LLM Applications for Engineers

🗂 Category: LLM APPLICATIONS

🕒 Date: 2025-11-25 | ⏱️ Read time: 22 min read

Drawing from two years of hands-on experience, this article outlines ten essential lessons for engineers building applications with Large Language Models. Gain practical insights and field-tested advice on structuring projects, optimizing workflows, and implementing effective evaluation strategies to successfully navigate the complexities of LLM development. This guide is for engineers looking to move from theory to production-ready applications.

#LLM #AIdevelopment #SoftwareEngineering #MLOps

📌 Learning, Hacking, and Shipping ML

🗂 Category: AUTHOR SPOTLIGHTS

🕒 Date: 2025-12-01 | ⏱️ Read time: 11 min read

Explore the ML lifecycle with Vyacheslav Efimov as he shares key insights for tech professionals. This discussion covers everything from creating effective data science roadmaps and succeeding in AI hackathons to the practicalities of shipping ML products. Learn how the evolution of AI is meaningfully changing the day-to-day workflows and challenges for machine learning practitioners in the field.

#MachineLearning #AI #DataScience #MLOps #Hackathon

📌 Overcoming the Hidden Performance Traps of Variable-Shaped Tensors: Efficient Data Sampling in PyTorch

🗂 Category: DEEP LEARNING

🕒 Date: 2025-12-03 | ⏱️ Read time: 10 min read

Unlock peak PyTorch performance by addressing the hidden bottlenecks caused by variable-shaped tensors. This deep dive focuses on the critical data sampling phase, offering practical optimization strategies to handle tensors of varying sizes efficiently. Learn how to analyze and improve your data loading pipeline for faster model training and overall performance gains.

#PyTorch #PerformanceOptimization #DeepLearning #MLOps

📌 How to Turn Your LLM Prototype into a Production-Ready System

🗂 Category: LLM APPLICATIONS

🕒 Date: 2025-12-03 | ⏱️ Read time: 15 min read

Transforming a promising LLM prototype into a production-ready system involves significant engineering challenges. This guide outlines the essential steps and best practices for moving beyond the experimental phase, focusing on building scalable, reliable, and efficient LLM applications for real-world deployment. Learn how to successfully operationalize your language model from concept to production.

#LLM #MLOps #ProductionAI #LLMOps

📌 On the Challenge of Converting TensorFlow Models to PyTorch

🗂 Category: DEEP LEARNING

🕒 Date: 2025-12-05 | ⏱️ Read time: 19 min read

Converting legacy TensorFlow models to PyTorch presents significant challenges but offers opportunities for modernization and optimization. This guide explores the common hurdles in the migration process, from architectural differences to API incompatibilities, and provides practical strategies for successfully upgrading your AI/ML pipelines. Learn how to not only convert but also enhance your models for better performance and maintainability in the PyTorch ecosystem.

#PyTorch #TensorFlow #ModelConversion #MLOps #DeepLearning