Deploying Secure Data Science Applications in the Cloud - From VMs to Serverless with AWS and Google Cloud

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
 

List of contents

Chapter 1: Initial Setup on Your AWS Account (aws.amazon.com) .- Chapter 2: SSH to the EC2 Instance with VSCode and Necessary Setup.- Chapter 3: Load Balancer on your AWS console.- Chapter 4: Domain Name and SSL Certificates.- Chapter 5. Deploying More Robust Applications (Jenkins, Flask, and Streamlit).- Chapter 6. Create and Secure your Subdomains.- Chapter 7. How to setup this infrastructure on Google Cloud Platform (GCP).- Chapter 8. Advanced Deployment in GCP: Auto Scaling and Load Balancing Across Global Regions.- Chapter 9. Serverless Deployment with Google Cloud Run.- Chapter 10. Serverless Deployment with AWS.- Chapter 11. Demo: Using Jenkins as an ETL/ELT Platform for Data Science.- Chapter 12. Demo: Streamlit.- Chapter 13. Demo: Flask.

About the author

LucasBraga is a Lead Data Scientist and AI Cloud Engineer with more than 10 years of experience. He served as Senior Data Scientist, Staff Data Scientist, and Manager at leading organizations such as DHL, Delivery Hero, and Wolt (a subsidiary of DoorDash).
He holds a master’s degree in Applied Statistics and Data Science from Kansas University, along with a Google Cloud certification and extensive experience with AWS. His expertise spans Data Science, Software Engineering, DevOps, Data Engineering, and Business Analytics, enabling him to deploy machine learning models securely and efficiently in production environments. Recognizing a critical industry gap in deploying applications that meet corporate security standards, Lucas combined his deep technical knowledge and teaching skills to create this book. It provides practical, security-focused guidance for Data Scientists and Software Engineers looking to enhance their deployment skills.

Summary

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
 

Product details

• Authors: Lucas Henrique Benevides e Braga
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Release: 11.01.2026
• EAN: 9798868817144
• ISBN: 9798868817144
• No. of pages: 250
• Illustrations: Approx. 250 p.
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; Künstliche Intelligenz, python, machine learning, aws, Artificial Intelligence, Programmier- und Skriptsprachen, allgemein, Cloud Computing, Google Cloud Platform, ML model deployment, DevOps Best Practices for Data Science, Data Science in Production, Secure Cloud Deployment for Data Science