Mastering Big Data Engineering Aws Gcp Azure Showdown
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Mastering Big Data Engineering: AWS, GCP, & Azure Showdown
Author: Muthuraman Saminathan
language: en
Publisher: Libertatem Media Private Limited
Release Date: 2024-02-16
In the rapidly evolving field of AI, operationalizing large language models (LLMs) has become a defining challenge. The LLMOps Advantage: Navigating the Future of AI is your comprehensive guide to mastering the deployment, monitoring, and scaling of LLMs in real-world applications. This book bridges the gap between model development and production, introducing readers to the specialized domain of LLMOps—a subset of MLOps tailored to the unique demands of large language models. From building scalable pipelines and optimizing inference workflows to ensuring compliance and security, this guide covers every aspect of operationalizing LLMs. Explore deployment strategies across platforms like AWS, Azure, GCP, and Hugging Face, learn about containerization and serverless architectures, and dive into tools for monitoring and observability such as Prometheus and Grafana. Through practical frameworks and case studies, the book provides actionable insights into managing performance metrics, addressing model drift, and leveraging distributed systems for scalability. Designed for data scientists, LLM engineers, and AI practitioners, The LLMOps Advantage also delves into ethical considerations, emerging trends like multi-modal models, and best practices for integrating LLMs with existing workflows. Whether you ' re fine-tuning models for specific tasks or scaling solutions to meet enterprise needs, this book equips you with the expertise to harness the full potential of LLMs. Stay ahead in the AI revolution with The LLMOps Advantage—your essential roadmap to mastering the future of large language model operations.
Mastering Big Data Engineering: AWS, GCP, & Azure Showdown
Author: Muthuraman Saminathan
language: en
Publisher: Libertatem Media Private Limited
Release Date: 2024-02-16
In the rapidly evolving field of AI, operationalizing large language models (LLMs) has become a defining challenge. The LLMOps Advantage: Navigating the Future of AI is your comprehensive guide to mastering the deployment, monitoring, and scaling of LLMs in real-world applications. This book bridges the gap between model development and production, introducing readers to the specialized domain of LLMOps—a subset of MLOps tailored to the unique demands of large language models. From building scalable pipelines and optimizing inference workflows to ensuring compliance and security, this guide covers every aspect of operationalizing LLMs. Explore deployment strategies across platforms like AWS, Azure, GCP, and Hugging Face, learn about containerization and serverless architectures, and dive into tools for monitoring and observability such as Prometheus and Grafana. Through practical frameworks and case studies, the book provides actionable insights into managing performance metrics, addressing model drift, and leveraging distributed systems for scalability. Designed for data scientists, LLM engineers, and AI practitioners, The LLMOps Advantage also delves into ethical considerations, emerging trends like multi-modal models, and best practices for integrating LLMs with existing workflows. Whether you ' re fine-tuning models for specific tasks or scaling solutions to meet enterprise needs, this book equips you with the expertise to harness the full potential of LLMs. Stay ahead in the AI revolution with The LLMOps Advantage—your essential roadmap to mastering the future of large language model operations.
Mastering Enterprise Performance Engineering: From Monoliths to Microservices
Author: Gaurav Rathor
language: en
Publisher: Xoffencer International Book Publication House
Release Date: 2025-06-28
Mastering Enterprise Performance Engineering: From Monoliths to Microservices is a comprehensive guide that explores the strategic, architectural, and engineering principles needed to build and maintain high-performance enterprise systems in the modern software era. As organizations evolve from legacy monolithic architectures to distributed microservices, the complexity of ensuring consistent performance, scalability, and reliability increases exponentially. This book provides an end-to-end performance engineering framework that integrates best practices across development, deployment, and operations. Beginning with foundational concepts of performance metrics, system bottlenecks, and load modeling, the book transitions into advanced topics such as distributed tracing, service mesh optimization, autoscaling policies, and performance-aware CI/CD pipelines. Readers will gain deep insights into capacity planning, cloud-native profiling, caching strategies, asynchronous processing, and real-time monitoring across microservices ecosystems. Case studies and real-world scenarios illustrate how to proactively diagnose and resolve performance degradation, even in highly dynamic environments. Designed for software architects, DevOps engineers, SREs, and technical leads, this book empowers professionals to shift performance left in the software lifecycle, adopt proactive observability, and ensure that systems not only function—but thrive— under demanding enterprise workloads. With a blend of theory, tooling, and actionable guidance, this book is essential reading for anyone navigating the shift from monoliths to microservices in pursuit of performance excellence.