Nidhi Sharma's contributions

Master AI workload orchestration to manage multiple models in production. Optimize performance, cost, and reliability for enterprise AI applications.

Explore Local-First AI Development: run enterprise models on Windows PCs for enhanced privacy, reduced costs, and lower latency.

Make your website AI-searchable with Schema.org and NLWeb. Enhance discoverability, user experience, and future-proof your content for intelligent systems.

Build cost-aware AI apps that scale efficiently. Master LLM costs, optimize RAG, implement tiering & budget controls in .NET.

Safely deploy AI in .NET with feature flags. Control rollouts, A/B test models, manage costs, and ensure governance for AI innovations.

Master prompt versioning & lifecycle management for enterprise AI. Ensure reliability, compliance, and scalability with structured governance.

Unlock enterprise knowledge with Azure AI Search. Build real-time, intelligent retrieval systems using semantic, vector, and hybrid search for RAG.

Build cross-platform desktop apps with Uno Platform & .NET 10. Share C#, XAML code for Windows, Linux, macOS, web, and mobile.

Master AI cost optimization with practical strategies for development teams. Reduce LLM, RAG, and embedding expenses for sustainable AI.

Reduce LLM costs with semantic caching. Reuse AI responses for similar queries, lowering expenses and latency in AI apps.

Hybrid search combines keyword and vector search for AI apps, improving accuracy and user experience. Essential for RAG.

Learn how to implement AI agent observability in .NET applications using monitoring, logging, tracing, OpenTelemetry, and best practices for production AI systems.

Learn how MCP Apps are transforming AI tool integration workflows by providing standardized access to APIs, databases, business systems, and enterprise resources through the Model Context Protocol.

Compare Semantic Kernel, LangChain, and AutoGen for enterprise .NET development. Learn the strengths, weaknesses, architecture differences, and best use cases for each AI framework.

Learn how to build autonomous QA testing agents for ASP.NET Core applications using AI, Semantic Kernel, and .NET testing frameworks to automate test generation, execution, and failure analysis.

Learn how to implement distributed caching in ASP.NET Core using Redis. Explore caching strategies, Redis integration, and best practices for scalable applications.

Learn how to build Retrieval-Augmented Generation (RAG) applications in .NET using ASP.NET Core, embeddings, vector databases, and large language models.

Learn C# Source Generators with practical examples. Discover how compile-time code generation improves performance and reduces boilerplate code in .NET applications.

Compare Azure Container Apps vs Azure Kubernetes Service (AKS). Learn the differences, use cases, costs, scaling options, and which Azure container platform is right for you.

Learn Agentic DevOps and how AI agents are transforming CI/CD pipelines through automated code reviews, testing, security analysis, deployment decisions, and incident response.

Learn the top AI agent security risks, including prompt injection, data exposure, and excessive permissions, along with best practices for building secure AI systems.

Learn FastAPI with step-by-step examples. Build high-performance REST APIs in Python using FastAPI, Pydantic, async programming, and automatic documentation.

Learn how to handle imbalanced datasets in machine learning using SMOTE, oversampling, undersampling, class weighting, and evaluation metrics with examples.

Learn Principal Component Analysis (PCA) for beginners with practical examples. Understand dimensionality reduction, explained variance, PCA implementation, and best practices.

Compare IAsyncEnumerable and IEnumerable in C#, including async streaming, memory use, performance trade-offs, and when to use each for data processing.