Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud

Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book―Amazon, Microsoft, Google, and PythonAnywhere.

You will work through a series of common Python data science problems in an increasing order of complexity. The practical projects presented in this book are simple, clear, and can be used as templates to jump-start many other types of projects. You will learn to create a web application around numerical or categorical predictions, understand the analysis of text, create powerful and interactive presentations, serve restricted access to data, and leverage web plugins to accept credit card payments and donations. You will get your projects into the hands of the world in no time.

Each chapter follows three steps: modeling the right way, designing and developing a local web application, and deploying onto a popular and reliable serverless cloud provider. You can easily jump to or skip particular topics in the book. You also will have access to Jupyter notebooks and code repositories for complete versions of the code covered in the book.

What You’ll Learn

• Extend your machine learning models using simple techniques to create compelling and interactive web dashboards
• Leverage the Flask web framework for rapid prototyping of your Python models and ideas
• Create dynamic content powered by regression coefficients, logistic regressions, gradient boosting machines, Bayesian classifications, and more
• Harness the power of TensorFlow by exporting saved models into web applications
• Create rich web dashboards to handle complex real-time user input with JavaScript and Ajax to yield interactive and tailored content
• Create dashboards with paywalls to offer subscription-based access
• Access API data such as Google Maps, OpenWeather, etc.
• Apply different approaches to make sense of text data and return customized intelligence
• Build an intuitive and useful recommendation site to add value to users and entice them to keep coming back
• Utilize the freemium offerings of Google Analytics and analyze the results
• Take your ideas all the way to your customer's plate using the top serverless cloud providers

Who This Book Is For

Those with some programming experience with Python, code editing, and access to an interpreter in working order. The book is geared toward entrepreneurs who want to get their ideas onto the web without breaking the bank, small companies without an IT staff, students wanting exposure and training, and for all data science professionals ready to take things to the next level.

將您的 Python 機器學習想法轉化為無伺服器的網絡應用程序，任何有互聯網連接的人都可以訪問。本書涵蓋了一些最受歡迎的無伺服器雲提供商，包括亞馬遜、微軟、谷歌和PythonAnywhere。

您將按照遞增的複雜性解決一系列常見的 Python 數據科學問題。本書中提供的實用項目簡單明了，可以用作啟動其他類型項目的模板。您將學習如何圍繞數值或分類預測創建網絡應用程序，了解文本分析，創建強大而互動的演示文稿，提供對數據的受限訪問，並利用網絡插件接受信用卡付款和捐款。您的項目將很快面向全球。

每個章節都遵循三個步驟：正確建模、設計和開發本地網絡應用程序，以及部署到流行且可靠的無伺服器雲提供商。您可以輕鬆跳轉到或跳過書中的特定主題。您還將獲得 Jupyter 筆記本和代碼存儲庫的訪問權限，以獲取書中涵蓋的代碼的完整版本。

您將學到以下內容：

- 使用簡單技術擴展機器學習模型，創建引人入勝且互動的網絡儀表板
- 利用 Flask 網絡框架快速原型化 Python 模型和想法
- 創建由回歸係數、邏輯回歸、梯度提升機、貝葉斯分類等驅動的動態內容
- 通過將保存的模型導出到網絡應用程序中，發揮 TensorFlow 的威力
- 使用 JavaScript 和 Ajax 創建豐富的網絡儀表板，處理複雜的實時用戶輸入，提供互動和定制的內容
- 創建具有付費牆的儀表板，提供訂閱式訪問
- 訪問 Google 地圖、OpenWeather 等 API 數據
- 應用不同方法來理解文本數據並返回定制的智能結果
- 構建直觀且有用的推薦網站，為用戶增加價值並吸引他們不斷回訪
- 利用 Google Analytics 的免費功能並分析結果
- 使用頂級無伺服器雲提供商將您的想法帶給客戶

本書適合具有一些 Python 編程經驗、代碼編輯和可正常運行的解釋器的人。本書適合希望將想法上網的企業家、沒有 IT 員工的小公司、希望獲得曝光和培訓的學生，以及所有準備將數據科學提升到更高水平的專業人士。