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.
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.
Explore our comprehensive courses on recommendation systems, RAG, LLM inference, and ads systems.