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Ship Machine Learning Web Apps with Python. Build and Deploy a Machine Learning Web App with Flask
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- FormatePub
- ISBN8259604773
- EAN9798259604773
- Date de parution29/06/2026
- Protection num.pas de protection
- Taille1 Mo
- Infos supplémentairesepub
- ÉditeurChiify
Résumé
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.
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.