Monetizing Machine Learning

New Release

September 17th, 2018

Machine learning is one of the most exciting and rapidly growing topics moving us forward in tech. The possibilities appear endless as its capabilities increase and artificial intelligence becomes vastly more augmented in our daily lives. For data science enthusiasts, students, or solo entrepreneurs, the concept of setting up their own ML system for their profit- or donation-based project might seem daunting. Our new release from Apress, Monetizing Machine Learning, quells those fears right away.

Through practical mini-projects that slowly progress in complexity, authors Manuel Amunategui & Mehdi Roopaei thoroughly break down the simple steps any individual with basic Python familiarity can use in order to create real-time ML models. You can easily alter and replace what is presented in these case studies with their application’s desired individual needs, like accepting credit card payments and donations, or utilizing helpful data from Google Maps and OpenWeather. Each chapter follows three steps: modeling your ML tasks, designing a web application, and making your work available to the public at large in a serverless way.

You're in expert hands with Amunategui and Roopaei, Vice President of Data Science at SpringML and a PhD postdoctoral fellow at the Open Cloud Institute, respectively. In Monetizing Machine Learning, they effortlessly combine three knowledge sets: prototyping via web applications, working with cloud providers, and the nuts-and-bolts statistics that serve as the basis of machine learning. Nearly every industry is booming with machine learning upgrades, and this new release puts that power into the hands of every individual with a vision.

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