國立高雄大學圖資館 |

Deploying secure data science applications in the cloudfrom vms to serverless with aws and google cloud /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deploying secure data science applications in the cloudby Lucas H. Benevides e Braga.
其他題名:	from vms to serverless with aws and google cloud /
作者:	Braga, Lucas H. Benevides e.
出版者:	Berkeley, CA :Apress :2025.
面頁冊數:	xix, 324 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Cloud computingSecurity measures.
電子資源:	https://doi.org/10.1007/979-8-8688-1715-1
ISBN:	9798868817151$q(electronic bk.)

Deploying secure data science applications in the cloudfrom vms to serverless with aws and google cloud /
Braga, Lucas H. Benevides e.

Deploying secure data science applications in the cloudfrom vms to serverless with aws and google cloud /[electronic resource] :by Lucas H. Benevides e Braga. - Berkeley, CA :Apress :2025. - xix, 324 p. :ill., digital ;24 cm.

Part I: Building the Foundation -- 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 -- Part II: Serverless Deployments -- Chapter 9. Serverless Deployment with Google Cloud Run -- Chapter 10. Serverless Deployment with AWS -- Part III: Jenkins, Streamlit and Flask Demos -- Chapter 11. Demo: Using Jenkins as an ETL/ELT Platform for Data Science -- Chapter 12. Demo: Streamlit -- Chapter 13. Demo: Flask.

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.

ISBN: 9798868817151$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-1715-1doiSubjects--Topical Terms:

404090
Cloud computing
--Security measures.

LC Class. No.: QA76.585

Dewey Class. No.: 005.8

Deploying secure data science applications in the cloudfrom vms to serverless with aws and google cloud /
LDR:04651nmm a2200325 a 4500 001 690611
003 DE-He213
005 20251022130425.0
006 m d
007 cr nn 008maaau
008 260409s2025 cau s 0 eng d
020 $a 9798868817151$q(electronic bk.)
020 $a 9798868817144$q(paper)
024 7 $a 10.1007/979-8-8688-1715-1 $2 doi
035 $a 979-8-8688-1715-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.585
072 7 $a UYQM $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 005.8 $2 23
090 $a QA76.585 $b .B813 2025
100 1 $a Braga, Lucas H. Benevides e. $3 1006165
245 1 0 $a Deploying secure data science applications in the cloud $h [electronic resource] : $b from vms to serverless with aws and google cloud / $c by Lucas H. Benevides e Braga.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2025.
300 $a xix, 324 p. : $b ill., digital ; $c 24 cm.
505 0 $a Part I: Building the Foundation -- 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 -- Part II: Serverless Deployments -- Chapter 9. Serverless Deployment with Google Cloud Run -- Chapter 10. Serverless Deployment with AWS -- Part III: Jenkins, Streamlit and Flask Demos -- Chapter 11. Demo: Using Jenkins as an ETL/ELT Platform for Data Science -- Chapter 12. Demo: Streamlit -- Chapter 13. Demo: Flask.
520 $a 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.
650 0 $a Cloud computing $x Security measures. $3 404090
650 0 $a Computer networks $x Security measures. $3 185597
650 0 $a Data protection. $3 202312
650 0 $a Computer security. $3 184416
650 1 4 $a Machine Learning. $3 833608
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Python. $3 763308
650 2 4 $a Cloud Computing. $3 777671
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/979-8-8688-1715-1
950 $a Professional and Applied Computing (SpringerNature-12059)