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⚡ Advanced Level

Build High-Performance Applications with AI

Master the art of building blazing-fast, scalable applications using AI-powered optimization techniques, advanced async patterns, distributed caching, cloud-native architecture, and AI-assisted code analysis.

50Hours
8Modules
10xFaster Apps
✓Certificate
Performance Goals of This Course
🚀 Response Time

Reduce API response time from seconds to milliseconds with smart caching and async patterns

📈 Throughput

Handle 100,000+ requests per second with distributed architecture and load balancing

💰 Cost

Cut AI API costs by 70%+ using prompt caching, model selection, and intelligent batching

🛡 Reliability

Build 99.9% uptime systems with resilience patterns, circuit breakers, and graceful degradation

Technologies & Tools

⚡ .NET 8/9 🔄 async/await + PLINQ 📦 Redis Cache 🐳 Docker + K8s ☁ Azure + AWS 📊 BenchmarkDotNet 🔍 dotTrace / VS Profiler ⚡ FastEndpoints 🤖 GitHub Copilot 🛠 k6 / Artillery 🔷 Polly (Resilience) 📈 Grafana + Prometheus

📋 Detailed Course Syllabus

Module 1: Performance Fundamentals & Profiling 5 Hours
  • Performance mindset —measuring before optimizing, Amdahl's Law
  • Profiling .NET apps with Visual Studio Performance Profiler and dotTrace
  • Memory profiling: dotMemory, finding memory leaks, large object heap issues
  • Benchmarking with BenchmarkDotNet —comparing algorithms scientifically
  • Understanding CPU-bound vs. I/O-bound operations and their implications
  • Performance counters, ETW events, and .NET diagnostics tools (dotnet-trace, dotnet-counters)
  • Identifying bottlenecks: N+1 queries, blocking I/O, chatty APIs, GC pressure
Module 2: Advanced Async Programming & Concurrency 7 Hours
  • Deep dive into async/await —ValueTask, ConfigureAwait, async state machines
  • Thread pool optimization —avoiding thread pool starvation
  • Parallel programming: Parallel.For, PLINQ, Parallel.ForEachAsync
  • Channels in .NET —producer-consumer patterns for high-throughput pipelines
  • IAsyncEnumerable —streaming data processing without memory pressure
  • SemaphoreSlim, CancellationToken, and cooperative cancellation patterns
  • Concurrent collections: ConcurrentDictionary, ConcurrentQueue, BlockingCollection
  • Deadlock detection and prevention strategies
Module 3: Caching Strategies for Extreme Performance 6 Hours
  • Caching fundamentals: cache-aside, write-through, write-behind, read-through
  • In-memory caching: IMemoryCache, MemoryCacheEntryOptions, eviction policies
  • Distributed caching with Redis: StackExchange.Redis, IDistributedCache
  • Cache-stampede prevention with SemaphoreSlim and GetOrCreateAsync patterns
  • Output caching in ASP.NET Core 7+ —response caching at the middleware level
  • AI response caching: semantic caching (cache by embedding similarity, not exact match)
  • CDN integration for static assets and API response caching
  • Cache invalidation strategies —event-driven invalidation with Redis Pub/Sub
Module 4: High-Performance API Design 6 Hours
  • Minimal APIs in .NET 8 —fastest API surface with zero overhead
  • Response compression: Brotli, Gzip for reducing payload sizes
  • Efficient JSON serialization: System.Text.Json source generation
  • gRPC in .NET —binary protocol for ultra-low latency inter-service calls
  • API pagination, cursor-based paging, keyset pagination for large datasets
  • Database query optimization: compiled queries, no-tracking queries, projections
  • Connection pooling and Dapper for high-throughput database access
  • Rate limiting in ASP.NET Core 7+ —protecting APIs with RateLimiterMiddleware
Module 5: AI-Powered Performance Optimization 7 Hours
  • Using GitHub Copilot for performance-aware code generation
  • AI code review: using LLMs to detect N+1 queries, inefficient algorithms, memory leaks
  • AI for intelligent caching: predict which data to cache using ML models
  • Semantic caching for LLM responses —reduce AI API costs by 60%+
  • AI-powered load testing: generating realistic test scenarios with LLMs
  • Anomaly detection with ML.NET for real-time performance monitoring
  • AI-assisted database query optimization: let AI suggest indexes and query rewrites
  • Using AI to analyze and interpret performance profiling reports
Module 6: Cloud-Native Performance Patterns 7 Hours
  • Horizontal vs. vertical scaling —when and how to scale
  • Microservices performance: service mesh, gRPC, MessageBus (Azure Service Bus, RabbitMQ)
  • Event-driven architecture with Azure Event Hubs / Kafka for high-throughput messaging
  • CQRS + Event Sourcing —separating read/write paths for extreme scale
  • Kubernetes scaling: HPA (Horizontal Pod Autoscaler), resource limits, affinity rules
  • Azure CDN, Azure Front Door —global content delivery and load balancing
  • Serverless with Azure Functions —auto-scaling for bursty workloads
  • Cost optimization: reserved instances, spot instances, consumption-based pricing
Module 7: Resilience, Reliability & Load Testing 6 Hours
  • Resilience patterns with Polly v8: retry, circuit breaker, timeout, bulkhead, fallback
  • Microsoft.Extensions.Http.Resilience —resilient HttpClient pipelines
  • Health checks in ASP.NET Core —readiness, liveness, startup probes
  • Load testing with k6 and Artillery —simulating 10,000+ concurrent users
  • Chaos engineering fundamentals —Netflix Chaos Monkey for .NET
  • Observability: structured logging (Serilog), distributed tracing (OpenTelemetry), metrics (Prometheus + Grafana)
  • Azure Application Insights —end-to-end transaction monitoring and performance analytics
Module 8: Capstone Project —Ultra-High-Performance AI Application 6 Hours
  • Design a production-grade AI-powered e-commerce API
  • Implement semantic search with vector DB, semantic caching, and streaming responses
  • Add distributed caching with Redis for product catalog and user sessions
  • Deploy to Kubernetes with auto-scaling based on custom metrics
  • Set up full observability: OpenTelemetry + Grafana + Prometheus dashboard
  • Load test to 50,000 requests/minute with k6, analyze results, optimize bottlenecks
  • Demonstrate 10x performance improvement before vs. after applying course techniques

Who Should Attend?

.NET/C# developers wanting to build faster, scalable applications

Backend engineers dealing with performance bottlenecks in production

Technical leads responsible for system architecture and performance

AI developers wanting to optimize LLM-powered application performance

DevOps engineers working on cloud infrastructure optimization

Senior developers preparing for architect-level roles

⚡ Build Applications That Are 10x Faster

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