Free Download Deploying and Scaling AI Models: From Local Prototypes to Cloud-Ready AI Applications by Vishal Uttam Mane
English | October 21, 2025 | ISBN: N/A | ASIN: B0FX4XLY4S | 318 pages | EPUB | 7.57 Mb
Deploying and Scaling AI Models: From Local Prototypes to Cloud-Ready AI Applications
Transform your AI projects into production-grade systems that scale.
In today's AI-driven world, building a model isn't enough, you must deploy, monitor, and scale it efficiently. This book is your end-to-end guide to taking machine learning and large language models (LLMs) from experimental notebooks to production-ready cloud environments.
You'll learn how to design reliable inference APIs, manage vector databases, automate workflows with CI/CD pipelines, and deploy optimized models across AWS, Azure, and Google Cloud, all while controlling costs and maintaining performance.What You'll LearnBuild inference-ready APIs with FastAPI, Docker, and container orchestrationIntegrate FAISS and vector databases for retrieval-augmented generation (RAG) systemsDesign MLOps pipelines with DVC, MLflow, and GitHub ActionsDeploy AI models to AWS SageMaker, GCP Vertex AI, and Azure MLMonitor drift, retrain automatically, and maintain model accuracyImplement serverless, edge, and distributed inference for scalabilityApply green compute and sustainability strategies for efficient AI operationsInside the BookFrom local prototypes to production APIs and vector databasesMLOps automation, CI/CD, and multi-cloud deploymentModel monitoring, drift detection, and retrainingReal-world case study, deploying a fine-tuned chatbot and a custom LLaMA modelThe future of AI infrastructure, edge AI, serverless LLMs, and green computingReady-to-use templates, Dockerfiles, FastAPI + FAISS examples, YAML CI/CD, cost comparison tables, and GPU troubleshooting guidesWho This Book Is ForMachine Learning Engineers looking to deploy models at scaleDevOps & Cloud Professionals managing AI pipelinesData Scientists transitioning from experimentation to productionStudents & Researchers learning modern MLOps and cloud AI deploymentWhy This Book Matters
Most AI projects fail to reach production, not because the models are weak, but because the infrastructure isn't ready.
This book bridges the gap between data science and software engineering, teaching you to build scalable, maintainable, and cost-optimized AI systems that work in the real world.
Whether you're deploying an internal chatbot or fine-tuning a custom LLM for the cloud, this hands-on guide gives you the blueprint to production success.
Turn your models into deployable, scalable AI systems and master the art of modern AI deployment.!
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