System Design & AI/ML Learning Platform
Master system design, AI theory, and machine learning with comprehensive topics, case studies, and interactive learning. From distributed systems to transformer architecture, RAG systems, LLMOps, and advanced ML concepts.
Understanding the difference between performance and scalability
Key metrics for system performance
Consistency, Availability, and Partition Tolerance
System reliability and availability characteristics
Internet Protocol addressing and network identification
Seven-layer network communication model
Transport layer protocols for reliable and fast communication
Patterns for achieving high availability in distributed systems
Distributing traffic across multiple servers for optimal performance
Hierarchical naming system that translates domain names to IP addresses
Grouping servers to work together as a single system
Intermediate servers for request handling and security
Real-time communication patterns for web applications
Choosing the right database type for your system requirements
Functional partitioning of databases by feature or service
Database consistency models for different system requirements
Database fundamentals and management systems
Relational databases with structured query language
Non-relational databases for flexible and scalable data storage
Database design techniques for data organization and performance
Architectural patterns for structuring applications
Single entry point for managing and routing API requests
Locating services in distributed systems
Layered architecture pattern for application design
Centralized integration pattern for enterprise applications
Storing frequently accessed data for quick retrieval
Global distribution of static content
Distributing data across nodes with minimal reorganization
Breaking large databases into smaller, manageable pieces
Intermediate servers for request handling and caching
Spatial data structures for location-based services
Virtualization technologies for resource isolation and efficiency
Data storage technologies and architectures
Breaking monoliths into independent services
Asynchronous communication between services
Storing data as a sequence of events rather than current state
Separating read and write operations for optimal performance
Essential security considerations
API technologies for different use cases and requirements
Preventing cascading failures in distributed systems
Modern authentication and authorization protocols
Unified authentication across multiple applications
Planning and implementing system recovery strategies
Controlling request frequency to protect system resources
Service level definitions for reliability and performance
Security protocols for encrypted communication
Core architecture of modern LLMs including attention mechanisms
Self-attention, multi-head attention, and positional encodings
Converting text to numerical representations for model processing
Controlling LLM output generation through temperature, top-k, and top-p sampling
Techniques for designing effective prompts to guide LLM behavior
Parameter-efficient fine-tuning techniques including LoRA and QLoRA
Retrieval-Augmented Generation for grounding LLMs in external knowledge
Autonomous AI agents that can reason, plan, and act using tools
Standardized protocol for AI model interaction with external tools and data sources
Containerization, API creation, and orchestration for LLM deployment
Tracking performance, detecting drift, and maintaining LLM system health
Continuous integration and deployment pipelines for GenAI applications
Quantization, pruning, distillation, and inference optimization techniques
Techniques for detecting and mitigating LLM hallucinations in production
RLHF, guardrails, and responsible AI practices for production systems
Storage and retrieval systems for embeddings in RAG applications
Data privacy, regulatory compliance, and security best practices
Design a service like Pastebin where users can store and share text
Design a social media platform like Twitter
Design a service like bit.ly that shortens URLs
Design a photo sharing service like Instagram
Design a video sharing platform like YouTube
Design a real-time suggestion service for search
Design a rate limiting system to throttle API requests
Design a web crawler that systematically browses the web
Design a real-time messaging service like WhatsApp
Design a video streaming service like Netflix
Design a ride-sharing service like Uber
Design a real-time fraud detection system with sub-100ms response time
Design a fashion recommendation system for complete outfit suggestions
Design a forecasting system for airport demand and driver wait times
Design a system to predict advertiser churn 14 days in advance
Design a food recommendation system to solve "what to order?" paralysis
Design a ride mode recommendation system balancing business goals
Design a demand forecasting system for fast-moving fashion industry
Design a machine learning system to optimize payment gateway selection
Design a system to predict accurate delivery dates balancing speed and reliability
Design a system to detect viral spam content before it spreads widely
Design a neural network system for automated home valuation at national scale
Design a multi-task learning system to consolidate hundreds of recommendation rankers
Design a system to predict real-time item availability across millions of items
Design a generative AI system for automated content creation with human oversight
Design an LLM system for automated root cause analysis and incident resolution
Design an AI pair programmer for real-time code generation in IDEs
Design an LLM system to generate observability queries from natural language
Design a system to organize massive content using multi-modal embeddings
Design a system to extract and prioritize guest interests from unstructured text
Design an A/B testing system for optimizing menu ranking across multiple countries
Master the structured approach from "Grok System Design Interview" used at top tech companies
Define the exact scope and goals of the system
Define the APIs that the system will expose
Estimate scale, storage, and bandwidth requirements
Define data entities and their relationships
Create a block diagram of core system components
Deep dive into 2-3 critical components
Find and resolve system bottlenecks and failure points