Data Science to Production

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 AWS and Google Cloud accounts, configuring security groups, and establishing robust 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 names, and securing applications with SSL (Secure Sockets Layer) certificates. The book introduces advanced deployment strategies using Jenkins, Flask, and Streamlit, enabling you to implement data pipelines, dashboards, and APIs that deliver machine learning predictions securely and efficiently. In addition, ithe book offers hands-on demonstrations for using Jenkins as an ETL platform, Streamlit as a dashboard service, and Flask for building APIs. For those interested in serverless deployments, 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

• Configure AWS and Google Cloud accounts, set up security groups, and use secure SSH via VSCode
• Deploy a basic HTTP Streamlit app to build skills for more complex production scenarios
• Understand load balancers, SSL certificates, and how to expose models and dashboads with Jenkins, Flask, and Streamlit
• Build machine learning models and deploy them effectively in real-world scenarios

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