# Machine Learning at Scale

> Become a x10 Machine Learning Engineer. In-depth technical content on building ML systems at scale — recommendation systems, LLM inference, MLOps, and production ML architecture.

Machine Learning at Scale is a technical resource for ML engineers who want to understand how top companies (YouTube, Netflix, LinkedIn, Meta, Airbnb, TikTok, Uber, Pinterest, DoorDash) build and operate machine learning systems in production. Content covers system design patterns, case studies from industry papers, LLM infrastructure, and ML career guidance.

## Courses

- [Recommendation Systems at Scale](https://machinelearningatscale.com/recommendation-systems-at-scale): End-to-end course on building production-grade recommendation systems
- [LLM Inference at Scale](https://machinelearningatscale.com/llm-inference-at-scale): Deep dive into serving large language models efficiently
- [RAG Systems at Scale](https://machinelearningatscale.com/rag-systems-at-scale): Building retrieval-augmented generation systems for production
- [Ads Systems at Scale](https://machinelearningatscale.com/ads-systems-at-scale): ML systems powering online advertising at scale

## Blog — Case Studies

- [Deep Neural Networks for YouTube Recommendations](https://machinelearningatscale.com/blog/youtube-deep-neural-networks-recommendations): How YouTube uses deep neural networks for its two-stage recommendation system serving 2B+ users
- [LinkedIn's MixLM: Achieving 10x Faster LLM Ranking](https://machinelearningatscale.com/blog/linkedin-mixlm-faster-llm-ranking): Embedding injection technique for faster LLM-based ranking at LinkedIn
- [Building LinkedIn's Semantic Search System](https://machinelearningatscale.com/blog/linkedin-semantic-search-system): From keyword search to semantic understanding at LinkedIn scale
- [xAI Recommendation System Architecture](https://machinelearningatscale.com/blog/xai-recommendation-system-architecture): Deep dive into Grok's content understanding and recommendation approach
- [Meta's GEM: LLM-Scale Architectures for Ads Recommendation](https://machinelearningatscale.com/blog/meta-gem-llm-ads-recommendation): How Meta brings LLM-scale models to ads ranking
- [Airbnb Embedding-Based Retrieval System](https://machinelearningatscale.com/blog/airbnb-embedding-retrieval-system): Engineering Airbnb's real-time embedding retrieval for search
- [vLLM at LinkedIn: Optimizing LLM Inference at Scale](https://machinelearningatscale.com/blog/vllm-linkedin-inference-optimization): How LinkedIn optimizes LLM inference costs with vLLM
- [Pinterest Recommendation System: Evolution Through the Years](https://machinelearningatscale.com/blog/pinterest-recommendation-evolution): Pinterest's journey from collaborative filtering to deep learning recommendations
- [TikTok's Real-Time Recommendation Algorithm at Scale](https://machinelearningatscale.com/blog/tiktok-recommendation-algorithm-scale): How TikTok serves personalized recommendations to billions in real-time
- [Uber's Optimal Feature Discovery for Machine Learning](https://machinelearningatscale.com/blog/uber-feature-discovery-ml): Feature engineering and discovery at Uber's ML platform
- [Netflix ML Platform: Media Understanding at Scale](https://machinelearningatscale.com/blog/netflix-ml-platform-media-understanding): How Netflix uses ML for content understanding and recommendations
- [Reddit's ML Model Deployment and Serving Architecture](https://machinelearningatscale.com/blog/reddit-ml-model-deployment-serving): Reddit's approach to ML deployment and online serving
- [Meta AI Platform: Building ML Infrastructure at Meta Scale](https://machinelearningatscale.com/blog/meta-ai-platform-infrastructure): Meta's internal ML platform and infrastructure design
- [DoorDash ML Monitoring: Building Observability for ML Systems](https://machinelearningatscale.com/blog/doordash-ml-monitoring-observability): Monitoring and observability practices for production ML at DoorDash
- [Uber's Continuous Model Deployment: ML DevOps at Scale](https://machinelearningatscale.com/blog/uber-continuous-model-deployment): How Uber automates model deployment in production
- [Wait Time Prediction at Yelp: Practical ML for Real-Time Estimates](https://machinelearningatscale.com/blog/yelp-wait-time-prediction-ml): Real-time wait time prediction using ML at Yelp
- [LinkedIn Real-Time Personalization: The Feature Store Approach](https://machinelearningatscale.com/blog/linkedin-real-time-personalization): How LinkedIn achieves near real-time personalization with feature stores
- [LinkedIn GenAI Platform Architecture](https://machinelearningatscale.com/blog/linkedin-genai-platform-architecture): How LinkedIn built its generative AI platform and lessons learned

## Blog — Design Patterns & Architecture

- [Memory Architectures for LLMs: Deep Dive](https://machinelearningatscale.com/blog/memory-architectures-llms-deep-dive): In-context, external, and parametric memory for large language models
- [Long Sequence Modeling for Recommendation Systems](https://machinelearningatscale.com/blog/long-sequence-recommendation-systems): Techniques for handling long user histories in recommendation models
- [Compound AI Systems Architecture](https://machinelearningatscale.com/blog/compound-ai-systems-architecture): Building production AI systems that go beyond single models
- [Two Tower Models in Industry: Complete Implementation Guide](https://machinelearningatscale.com/blog/two-tower-models-industry-guide): How two-tower architectures power retrieval at scale across top companies
- [Feature Stores in an Embedding World: Modern Architecture](https://machinelearningatscale.com/blog/feature-stores-embedding-world): Designing feature stores for modern ML and embedding-heavy workloads
- [Late Interaction Retrieval: ColBERT and ColPali Explained](https://machinelearningatscale.com/blog/late-interaction-retrieval-colbert): How late interaction models improve retrieval quality vs bi-encoders
- [RLHF with Rubrics as Rewards: A Practical Approach](https://machinelearningatscale.com/blog/rlhf-rubrics-rewards-guide): Using structured rubrics instead of learned reward models for RLHF
- [Towards Large-Scale Generative Ranking in ML](https://machinelearningatscale.com/blog/large-scale-generative-ranking-ml): Applying generative models to the ranking problem at scale
- [Agent Context Engineering: Optimizing LLM Agent Performance](https://machinelearningatscale.com/blog/agent-context-engineering-guide): How to engineer context windows for high-performance LLM agents
- [Multi-Agent LLM Systems: Architecture Patterns for Production](https://machinelearningatscale.com/blog/multi-agent-llm-systems-architecture): Building reliable multi-agent systems with LLMs
- [Mixture of Experts: How DeepSeek and Mistral Scale LLMs](https://machinelearningatscale.com/blog/mixture-of-experts-scaling-llms): MoE architecture explained and how it enables efficient LLM scaling
- [GraphRAG: Microsoft's Knowledge Graph-Enhanced Retrieval](https://machinelearningatscale.com/blog/graphrag-knowledge-graph-retrieval): How GraphRAG improves retrieval quality using knowledge graphs
- [LLM Evaluation at Scale: Beyond Benchmarks](https://machinelearningatscale.com/blog/llm-evaluation-at-scale): Production-oriented LLM evaluation metrics and techniques
- [Flash Attention Explained: Making Transformers Fast](https://machinelearningatscale.com/blog/flash-attention-explained): How FlashAttention accelerates transformers without approximation
- [DPO Explained: Direct Preference Optimization vs RLHF](https://machinelearningatscale.com/blog/dpo-direct-preference-optimization-explained): How DPO simplifies alignment compared to RLHF
- [Synthetic Data for LLM Training: Techniques and Trade-offs](https://machinelearningatscale.com/blog/synthetic-data-generation-llms): Industry practices for generating and using synthetic training data
- [Model Merging: DARE, TIES, and Model Soups](https://machinelearningatscale.com/blog/model-merging-dare-ties-guide): How to combine fine-tuned models without retraining
- [Vision-Language Models: Architecture and Training](https://machinelearningatscale.com/blog/vision-language-models-architecture): How multimodal models like GPT-4o process images and text together
- [Continuous Batching and PagedAttention: How vLLM Serves LLMs](https://machinelearningatscale.com/blog/continuous-batching-paged-attention-vllm): Key techniques enabling 10x LLM serving throughput
- [Long Context LLMs: RoPE Scaling and Retrieval Approaches](https://machinelearningatscale.com/blog/long-context-llms-techniques-tradeoffs): Techniques and trade-offs for extending LLM context windows
- [LLM Scaling Laws: Chinchilla and Compute-Optimal Training](https://machinelearningatscale.com/blog/scaling-laws-llms-chinchilla-guide): Understanding scaling laws to train LLMs efficiently
- [Mamba and State Space Models: Challenging Transformers](https://machinelearningatscale.com/blog/mamba-state-space-models-explained): How Mamba's SSM architecture competes with attention-based models
- [DeepSeek-R1: Reinforcement Learning for Reasoning in LLMs](https://machinelearningatscale.com/blog/deepseek-r1-reinforcement-learning-reasoning): How RL techniques unlock chain-of-thought reasoning in language models
- [Speculative Decoding: 3-5x LLM Throughput Without Model Changes](https://machinelearningatscale.com/blog/speculative-decoding-llm-inference): How speculative decoding accelerates autoregressive LLM inference
- [KV Cache Optimization: Engineering Core of Efficient LLM Serving](https://machinelearningatscale.com/blog/kv-cache-optimization-llm-serving): Deep dive into KV cache management for production LLM serving
- [Test-Time Compute Scaling: The New Dimension of AI Performance](https://machinelearningatscale.com/blog/test-time-compute-scaling): How scaling inference compute improves model performance

## Blog — Tutorials & Guides

- [Testing Machine Learning Systems: A Comprehensive Guide](https://machinelearningatscale.com/blog/testing-machine-learning-systems): Strategies and tools for testing ML models and pipelines in production
- [Active Learning in ML: Efficient Data Labeling](https://machinelearningatscale.com/blog/active-learning-ml-guide): How active learning reduces labeling cost while maximizing model performance
- [Evaluating Ranking Models: Offline and Online Metrics](https://machinelearningatscale.com/blog/evaluating-ranking-models-guide): Complete guide to ranking model evaluation from NDCG to online A/B tests
- [Production ML: A Reality Check on MLOps Practices](https://machinelearningatscale.com/blog/production-ml-mlops-reality-check): Honest look at what MLOps looks like in real production environments

## Blog — Career

- [Getting Into Machine Learning in 2026: A Practical Roadmap](https://machinelearningatscale.com/blog/ml-career-2026-getting-started): Step-by-step guide for breaking into ML engineering in 2026
- [Negotiating ML Engineering Offers: A Complete Guide](https://machinelearningatscale.com/blog/negotiating-ml-job-offers): How to negotiate compensation for ML engineering roles at top companies
- [Technical Debt in ML Systems: Why the Interest Rate is So High](https://machinelearningatscale.com/blog/ml-technical-debt-high-interest): Understanding and managing technical debt specific to ML systems

## Additional Resources

- [ML System Design Patterns](https://machinelearningatscale.com/patterns): Reusable architectural patterns for ML systems
- [ML Systems Case Studies](https://machinelearningatscale.com/ml-systems-case-studies): Collection of real-world ML system case studies from top companies
- [AI Consulting](https://machinelearningatscale.com/ai-consulting): ML system design and consulting services
- [Tools](https://machinelearningatscale.com/tools): Curated tools for ML engineers