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. ...

Building Scalable Serverless AI/ML Pipelines As the demand for artificial intelligence (AI) and machine learning (ML) applications continues to grow, the need for scalable and efficient pipelines has never been more pressing. In this article, we will explore the benefits and challenges of building scalable serverless AI/ML pipelines and provide a step-by-step guide on how to implement them. Introduction Serverless architecture is a key enabler for scalable AI/ML pipelines, allowing data engineers to focus on building and deploying applications without managing infrastructure. By leveraging serverless computing services like AWS Lambda, Google Cloud Functions, and Azure Functions, we can create scalable and cost-effective pipelines that can handle large volumes of data. ...

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. ...

Implementing Responsible AI in Recommendation Systems: A Step-by-Step Guide Introduction Recommendation systems are ubiquitous in modern applications, influencing everything from our social media feeds to our online shopping experiences. However, these systems can perpetuate biases and lack transparency, leading to unintended consequences. In this article, we’ll explore the importance of responsible AI in recommendation systems and provide a step-by-step guide on implementing strategies for mitigating bias and ensuring transparency. Prerequisites Basic understanding of recommendation systems and their applications Familiarity with machine learning concepts and Python programming language Access to a dataset for experimentation (e.g., MovieLens, Book-Crossing) Identifying and Understanding Bias in Recommendation Systems Bias in recommendation systems refers to the unfair or discriminatory treatment of certain groups or individuals. There are several types of bias that can occur in recommendation systems, including: ...