Manuel Amunategui - Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud
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- Book:Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud
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Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud: summary, description and annotation
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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 bookAmazon, 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 Youll 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 customers 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.
Manuel Amunategui: author's other books
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Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/9781484238721 . For more detailed information, please visit www.apress.com/source-code .
A few decades ago, as a kid learning to program, I had an ASCII gaming book for my Apple II (of which the name eludes me) that started each chapter with a picture of the finished game. This was the teaser and the motivator in a book that was otherwise made up of pages and pages of nothing else but computer code. This was years before GitHub and the Internet. As if it were only yesterday, I remember the excitement of racing through the code, copying it line-by-line, fixing typos and wiping tears just to play the game. Today, a lot has changed, but even though the code is downloadable, we put a screenshot of the final product at the beginning of each chapter, so you too can feel the motivation and excitement of working through the concepts.
This book will guide you through a variety of projects that explore different Python machine learning ideas and different ways of transforming them into web applications. Each chapter ends with a serverless web application accessible by anyone around the world with an Internet connection. These projects are based on classic and popular Python data science problems that increase in difficulty as you progress. A modeling solution is studied, designed, and an interesting aspect of the approach is finally extended into an interactive and inviting web application.
Being a data scientist is a wonderful profession, but there is a troubling gap in the teaching material when trying to become one. Data science isnt about statistics and modeling; it is about fulfilling human needs and solving real problems. Not enough material tackles the big picture. it seems that whenever you start talking about the big picture, you have to sign a non-disclosure agreement (NDA). This is an essential area of study and if you are like me, you need to understand why you are doing something in order to do it right. These arent difficult topics, especially when you use the right tools to tackle them.
The classic data science Venn diagram next to my updated version
Mehdi and I worked hard on the content of this book. We took our time to develop the concepts, making sure they were of practical use to our reader (i.e., our customer always keep the customer in mind at all times). I built the material and Mehdi edited it. This is an ambitious book in terms of scope and technologies covered. Choices and compromises had to be made to focus on the quickest ways of getting practical use out of the material. The tools are constantly changing. Some things in this book are going to be stale by the time you read them, and that is OK (you can go to the GitHub repo for updates). Here, everything changes all the time, but things tend to change for the better! So, learning new tricks often means learning better, faster, and more powerful ways to do things. This book will not only show you how to build web applications but also point you in the right direction to deepen your knowledge in those areas of particular interest.
If this was a class, Id have you sign a compete agreement : yes, the opposite of a non-compete. I would have you go through this book, understand the tools, and then copy them and make them your own. These are meant to be templates to quickly get your platforms up and running to focus on the bigger things, to build impactful tools for your customers and friends. When you understand this, thats the day you graduate with all the entitlements and privileges of being called a data science professional.
Cloud providers have gone to great efforts to improve web hosting solutions and bring costs down. The recent advent of the serverless option, which abstracts a large swath of the configuring process, is available on three of the four cloud providers covered in this book. This means you can get your projects up and running on fully managed platforms, with automatic load-balancing, throughput scaling, fast deployments, etc., without having to select, configure, or worry about any of it. The level of disengagement with these architectural and monitoring options is up to you. You can choose what you want to control and what you want to delegate to the provider. One thing is guaranteed: the site will automatically adjust with traffic and offer unparalleled uptime.
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