Commensurabilities among Lattices in PU

This step-by-step guide is for Data Scientists, ML engineers, and DevOps practitioners who need to turn prototypes into secure, scalable production services on AWS and Google Cloud. With step-by-step instructions and practical examples, this book bridges the gap between building Data Science applications and Machine Learning models, and deploying them effectively in real-world scenarios The book begins with an introduction to essential cloud concepts, providing detailed guidance on setting up a virtual machine (VM) on AWS—and later on Google Cloud—to serve applications. This includes configuring security groups and establishing secure SSH (Secure Shell) connections using VSCode (Visual Studio Code). You will learn how to deploy a dummy HTTP Streamlit application as a foundational exercise before advancing to more complex setups. Subsequent chapters dive deeper into key deployment practices, such as configuring load balancers, setting up domain and subdomain names, and securing applications with SSL (Secure Sockets Layer) certificates. The book introduces more advanced deployment strategies using Docker containers and Nginx as a reverse proxy, as well as secure serverless deployments of Jenkins, Flask, and Streamlit. You’ll also learn how to train machine learning models and use Flask to build APIs that serve those models in production. In addition, the book offers hands-on demonstrations for using Jenkins as an ETL platform, Streamlit as a dashboard service, and Flask for API development. For those interested in serverless architectures, it provides detailed guidance on using AWS ECS (Elastic Container Service) Fargate and Google Cloud Run to build scalable and cost-effective solutions. By the end of this book, you will possess the skills to deploy and manage data science applications on the cloud with confidence. Whether you are scaling a personal project or deploying enterprise-level solutions, this book is your go-to resource for secure and seamless cloud deployments. What You Will Learn Deploy end-to-end data science applications with a strong foundation in cloud infrastructure setup, including VM provisioning, SSH access, security groups, SSL configuration, load balancers, and domain management for secure, real-world deployments Use industry-known tools such as Docker, Nginx, Flask, Streamlit, and Jenkins to build secure, scalable services Understand how to structure and expose machine learning models via APIs for production use Explore modern serverless architectures with AWS Fargate and Google Cloud Run to scale efficiently with minimal overhead Develop a cloud deployment mindset grounded in doing things from scratch—before adopting abstracted solutions   Who This Book Is For Beginning to intermediate professionals with a basic understanding of Python, including Data Scientists, ML Engineers, Data Engineers, and Data Analysts who aim to securely deploy their projects in production environments, and individuals working on both personal projects and enterprise-level solutions, leveraging AWS and Google Cloud setups