Ship Machine Learning Web Apps with Python
Build and Deploy a Machine Learning Web App with FlaskBy Nathan Jansen
Length8h 27m
About this audiobook
deploy machine learning models python web app deployment Ready to stop Jupyter and start shipping? Ship Machine Learning Web Apps with Python is your hands-on guide to taking a trained model from a notebook to a live, interactive web interface. Nathan Jansen walks you through every step: setting up a Flask or FastAPI backend, designing a clean frontend with HTML and JavaScript, handling user input, serving predictions in real time, and deploying to the cloud. No fluff—just practical, project-based learning for beginners and pros alike. You'll build a complete, user-friendly ML web app from scratch, learn to manage model versioning and caching, and discover how to monitor performance in production. By the end, you'll have a portfolio-ready project and the confidence to ship your own models. This book is ideal for data scientists, Python developers, and anyone who wants to turn a trained model into a tool people actually use. Whether you're a student, a freelancer, or a team lead, you'll find actionable techniques to bridge the gap between research and real-world impact. Competitor authors: [placeholder] and [placeholder] offer alternative approaches, but this book focuses on end-to-end deployment with modern, lightweight tools.
This hands-on Python guide is written to be used at the keyboard: every concept is paired with something you can run, adapt, and keep. You move from first principles to real, working results, with the common errors and fixes called out along the way so you are never stuck for long.
Audiobook details
GenreTechnology
Length8 hrs 27 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
LanguageEnglish
Table of contents
1Introduction
12Chapter 10 — Storing Predictions: Database Integration with SQLite/PostgreSQL
2Preface
13Chapter 11 — RESTful APIs for External Access
3Chapter 1 — The Big Picture: Your First ML Web App
14Chapter 12 — Testing Your Web App
4Chapter 2 — Data Wrangling: From CSV to Clean Dataset
15Chapter 13 — Error Handling and Logging
5Chapter 3 — Exploratory Data Analysis and Visualization
16Chapter 14 — Performance Optimization: Caching and Asynchronous Tasks
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6Chapter 4 — Building a Baseline Model with Scikit-Learn
17Chapter 15 — Deployment to Production: Heroku/AWS
7Chapter 5 — Improving the Model: Feature Engineering and Hyperparameter Tuning
18Chapter 16 — Containerization with Docker
8Chapter 6 — Introducing Flask: Your Web Framework Foundation
19Chapter 17 — Monitoring and Maintenance
9Chapter 7 — Serving the Model: Loading and Predicting in Flask
20Chapter 18 — Next Steps: Extending Your App
10Chapter 8 — User Interfaces: Forms and Templates
21About the Author
11Chapter 9 — Authentication and User Management
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