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Flask for Data Science and Machine Learning: Build and Deploy Data Science and Machine Learning Projects with Python, Flask, and Scikit-Learn
Flask is a lightweight, high-performance Python web framework favored by data scientists and machine learning engineers for turning notebooks into deployable web applications and intelligent APIs. When combined with tools like Scikit-learn, TensorFlow, and Plotly, Flask becomes a powerful launchpad for real-world AI projects—from model inference services to interactive data dashboards.
Written by an experienced developer and AI practitioner, this book was designed based on reviews of top-selling machine learning deployment books and the practical needs of professionals working in real-world environments. Every chapter is grounded in industry-proven patterns, best practices, and the latest tools used in data science and MLOps pipelines.
Flask for Data Science and Machine Learning is a hands-on, comprehensive guide that teaches you how to build, scale, and deploy ML models using Python and Flask. Whether you're working on classification, regression, or computer vision tasks, this book helps you wrap your models into production-ready APIs and full-stack applications. Learn to design clean interfaces, visualize data interactively, monitor performance, and deploy seamlessly with Docker and CI/CD pipelines.
Clear step-by-step tutorials for building ML APIs with Flask
Practical examples using Scikit-learn, TensorFlow, and XGBoost
Real-world ML use sentiment analysis, image classification, and more
Integration of Plotly, Seaborn, and Chart.js for visual insights
Testing, monitoring, and profiling your models
Deployment with Docker, Gunicorn, GitHub Actions, and MLOps workflows
Advanced microservices, blueprints, A/B testing, CI/CD, and edge deployment
This book is for data scientists, ML engineers, full-stack Python developers, and AI enthusiasts who want to learn how to deploy machine learning models effectively. Basic familiarity with Python, machine learning libraries (like Scikit-learn or Pandas), and web development fundamentals is recommended.
If you’re tired of models that never make it beyond Jupyter, this book accelerates your transition from experimentation to real-world deployment—without spending months wrestling with DevOps or web frameworks. In just a few days, you’ll go from prototype to production-ready ML web services.
Stop keeping your ML models locked in notebooks. Learn how to serve predictions, build full-stack AI dashboards, and confidently deploy to the cloud with Flask for Data Science and Machine Learning.
Get your copy now to master Flask, serve smarter AI, and future-proof your ML career today.
Written by an experienced developer and AI practitioner, this book was designed based on reviews of top-selling machine learning deployment books and the practical needs of professionals working in real-world environments. Every chapter is grounded in industry-proven patterns, best practices, and the latest tools used in data science and MLOps pipelines.
Flask for Data Science and Machine Learning is a hands-on, comprehensive guide that teaches you how to build, scale, and deploy ML models using Python and Flask. Whether you're working on classification, regression, or computer vision tasks, this book helps you wrap your models into production-ready APIs and full-stack applications. Learn to design clean interfaces, visualize data interactively, monitor performance, and deploy seamlessly with Docker and CI/CD pipelines.
Clear step-by-step tutorials for building ML APIs with Flask
Practical examples using Scikit-learn, TensorFlow, and XGBoost
Real-world ML use sentiment analysis, image classification, and more
Integration of Plotly, Seaborn, and Chart.js for visual insights
Testing, monitoring, and profiling your models
Deployment with Docker, Gunicorn, GitHub Actions, and MLOps workflows
Advanced microservices, blueprints, A/B testing, CI/CD, and edge deployment
This book is for data scientists, ML engineers, full-stack Python developers, and AI enthusiasts who want to learn how to deploy machine learning models effectively. Basic familiarity with Python, machine learning libraries (like Scikit-learn or Pandas), and web development fundamentals is recommended.
If you’re tired of models that never make it beyond Jupyter, this book accelerates your transition from experimentation to real-world deployment—without spending months wrestling with DevOps or web frameworks. In just a few days, you’ll go from prototype to production-ready ML web services.
Stop keeping your ML models locked in notebooks. Learn how to serve predictions, build full-stack AI dashboards, and confidently deploy to the cloud with Flask for Data Science and Machine Learning.
Get your copy now to master Flask, serve smarter AI, and future-proof your ML career today.
272 pages, Kindle Edition
Published July 3, 2025
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