Deep dives into ML systems at scale. Case studies from top tech companies, design patterns, and practical guides for machine learning engineers.
Learn how YouTube uses deep neural networks to power its recommendation system serving billions of users. Explore the two-stage architecture, candidate generation, and ranking models.
Discover how LinkedIn achieved 10x faster LLM-based ranking using their innovative MixLM architecture with embedding injection techniques.
Explore how LinkedIn transformed its job search from keyword matching to semantic understanding using embeddings and neural retrieval.
An in-depth analysis of xAI's recommendation system architecture powering Grok's personalized content delivery.
How Meta integrated LLM-scale architectures into their ads recommendation system through the GEM (Generative Embeddings Model) framework.
A comprehensive guide to how Airbnb built their embedding-based retrieval system for search and recommendations.
Deep dives into ML systems at top tech companies
Learn how YouTube uses deep neural networks to power its recommendation system serving billions of users. Explore the two-stage architecture, candidate generation, and ranking models.
Discover how LinkedIn achieved 10x faster LLM-based ranking using their innovative MixLM architecture with embedding injection techniques.
Explore how LinkedIn transformed its job search from keyword matching to semantic understanding using embeddings and neural retrieval.
An in-depth analysis of xAI's recommendation system architecture powering Grok's personalized content delivery.
How Meta integrated LLM-scale architectures into their ads recommendation system through the GEM (Generative Embeddings Model) framework.
A comprehensive guide to how Airbnb built their embedding-based retrieval system for search and recommendations.
How LinkedIn leveraged vLLM to achieve efficient LLM inference for their GenAI platform serving millions of requests.
Explore the various memory architectures for LLMs including Mem0, MemGPT, and other approaches to extending LLM context.
Trace the evolution of Pinterest's recommendation system from early heuristics to modern deep learning approaches.
How to effectively model long user behavior sequences for better recommendations using transformers and efficient attention.
Inside look at LinkedIn's GenAI platform architecture, covering model serving, prompt management, and production deployment.
Learn how to architect compound AI systems that combine multiple models, retrievers, and tools for complex tasks.
How LinkedIn achieves near real-time personalization using their online feature store architecture.
How TikTok's recommendation algorithm processes billions of videos to deliver personalized content in real-time.
How Uber automatically discovers and ranks the most important features for their ML models at scale.
Inside Netflix's ML platform for media understanding including video analysis, content tagging, and personalization.
How Reddit deploys and serves machine learning models for content ranking, recommendations, and moderation.
Inside Meta's AI platform infrastructure supporting training and serving for billions of users.
How DoorDash monitors their ML systems to ensure reliability and catch issues before they impact customers.
How Uber implements continuous deployment for ML models with automated validation and safe rollouts.
How Yelp built their wait time prediction system to help diners plan their restaurant visits.
A deep dive into DeepSeek-R1's training methodology — using pure RL to teach LLMs to reason step-by-step, and what this means for the future of AI at scale.
Architectural patterns and best practices for ML systems
Explore how generative models are transforming ranking systems from discriminative to generative approaches.
Honest assessment of what works and what doesn't in MLOps based on real-world production experience.
Learn how to engineer context effectively for LLM agents to improve task completion and reduce hallucinations.
Comprehensive guide to implementing two-tower models for retrieval including training, serving, and optimization.
How to use structured rubrics instead of human preferences for more consistent and interpretable RLHF.
Understanding late interaction retrieval methods including ColBERT and ColPali for efficient semantic search.
How feature stores are evolving to support embedding-based ML systems with vector storage and real-time updates.
Strategies and patterns for testing ML systems including unit tests, integration tests, and model validation.
How to use active learning to reduce labeling costs while maintaining model quality through intelligent sample selection.
Complete guide to evaluating ranking models including offline metrics, online experiments, and bridging the gap.
A practical guide to speculative decoding — the inference optimization technique used by Google, Meta, and others to dramatically accelerate LLM serving without any model quality loss.
How to design, build, and operate multi-agent LLM systems at scale — covering orchestration patterns, communication protocols, failure handling, and lessons from production deployments.
A technical deep dive into Mixture of Experts (MoE) architecture — how sparse activation, expert routing, and load balancing enable trillion-parameter models that cost less to serve than dense alternatives.
How Microsoft's GraphRAG moves beyond simple vector search to answer complex multi-hop questions using knowledge graphs — and what it means for production RAG systems.
A deep technical dive into Mamba and structured state space models (SSMs) — the architecture achieving transformer-quality results with linear complexity and faster inference on long sequences.
A deep dive into FlashAttention — the IO-aware exact attention algorithm that makes training and inference 2-4x faster and 5-20x more memory-efficient without any approximation.
A deep technical dive into Direct Preference Optimization (DPO) — how it simplifies RLHF by eliminating the reward model and RL loop, why it works, and when to use it over PPO-based training.
How leading labs use synthetic data to train better LLMs — from self-instruct to distillation to execution-verified synthetic code. Covers the techniques, quality control, and when synthetic data helps vs. hurts.
A deep dive into model merging techniques — how combining weights from multiple fine-tuned models can create a single model that outperforms all of them, without any additional training.
A deep technical dive into how vision-language models (VLMs) work — from CLIP and early VLMs to LLaVA, Qwen-VL, and GPT-4o. Covers architecture, training stages, and production deployment.
A deep dive into the systems innovations behind vLLM — continuous batching, PagedAttention, and KV cache management — that enable serving LLMs at dramatically higher throughput than naive implementations.
How modern LLMs extend context windows to 128K, 1M, and beyond — covering RoPE scaling, positional extrapolation, attention approximations, and when long context beats RAG.
A deep dive into neural scaling laws — how they predict model performance, what Chinchilla changed about how we train LLMs, and the emerging debate about whether scaling is hitting limits.
Explore our comprehensive courses on recommendation systems, RAG, LLM inference, and ads systems.