Modern Large Language Models: Architecture, Fine-Tuning, and Production Deployment Note: This guide is based on the original “Attention Is All You Need” paper (Vaswani et al., 2017), Hugging Face Transformers documentation, and production patterns from LLM providers including OpenAI, Anthropic, and Meta. All code examples use documented APIs and follow industry best practices for LLM deployment. Large Language Models (LLMs) have evolved from academic curiosities to production systems powering ChatGPT, Claude, GitHub Copilot, and enterprise search. Built on the transformer architecture, modern LLMs contain billions of parameters and demonstrate emergent capabilities including reasoning, code generation, and multi-turn conversation. ...

Deep Learning Model Optimization: From Training to Production Deployment Note: This guide is based on PyTorch quantization documentation (v2.1+), TensorFlow Model Optimization Toolkit documentation, ONNX specification v1.14, and NVIDIA TensorRT best practices. All code examples use production-tested optimization techniques and include performance benchmarks. Model optimization bridges the gap between research and production. A ResNet-50 trained in FP32 consumes 98MB and runs at 15ms inference on CPU. With INT8 quantization, the same model shrinks to 25MB and runs at 4ms—enabling deployment on edge devices, reducing cloud costs, and improving user experience. ...

Scalable Serverless AI/ML Pipelines: A Production Guide Research Disclaimer: This guide is based on AWS SDK for Python (boto3) v1.34+, SageMaker Python SDK v2.200+, and AWS Step Functions State Language (Amazon States Language) official documentation. All code examples follow AWS Well-Architected Framework for ML workloads and include production-tested patterns for serverless deployment, monitoring, and cost optimization. Serverless ML pipelines eliminate infrastructure management while providing automatic scaling, pay-per-use pricing, and high availability. This guide covers production-ready patterns for deploying ML models using AWS Lambda, SageMaker, Step Functions, and EventBridge, with complete working examples that you can deploy immediately. ...

Scaling Mobile App Development with React Native: A Comprehensive Guide Note: This guide is based on the official React Native documentation (v0.73), Expo SDK 50 documentation, and documented security best practices from OWASP Mobile Security Project. All code examples use official React Native APIs and follow the React Native community guidelines. React Native has evolved from a Facebook experiment into the production framework powering apps like Instagram, Facebook, Discord, and Microsoft Teams. With code sharing between iOS and Android reaching 95%+ in well-architected apps, React Native offers compelling economics for mobile development while maintaining near-native performance. ...

Container Networking Deep Dive: From Network Namespaces to Kubernetes Note: This guide is based on the Linux kernel networking documentation, Docker networking documentation (v24+), Kubernetes networking model documentation (v1.28+), and CNI specification v1.0. All examples use documented networking primitives and follow production container networking patterns. Container networking is fundamental to modern cloud-native applications. Understanding how packets flow from pod to pod, how services load-balance traffic, and how network policies enforce security requires knowledge of Linux networking primitives, Container Network Interface (CNI) plugins, and Kubernetes networking abstractions. ...