The Internet of Things (IoT) may be the most disruptive set of technologies in a generation, and the most disruptive tech company in the world -- Amazon -- is building the IoT strategies that will influence every organization and sector. We can all learn from these strategies. In this detailed analysis of IoT and Amazon's and other leading companies approach to it, John Rossman guides readers with practical insights and recommendations into the strategies and mindset transforming business and society. "John has laid out a blueprint not only for an enterprise wanting to understand how sensors embedded in their business can innovate old ways of working while also providing an excellent path for individuals wanting to start their own IoT business. The book is not only a reference tool but also paints a story around innovation and customer centricity to challenge the reader to think differently in solving problems."
Recently, there is increasing attention towards machine learning and its application within personal and business contexts. "The study of computer algorithms that improve automatically through experience" Machine learning leverages mathematical models and big data to solve business problems. This requires two key support areas of data engineering and data science. Within data engineering, the evolution of cloud computing has allowed big data to be stored inexpensively. Within data science, the rise of data scientists and data science tools have allowed better ease of model building and exploration. However, machine learning is NOT the summit of a data analytics evolution journey.
Machine learning and artificial intelligence are technologies that have evolved rapidly in the last decade. Most people today are familiar with intelligent voice assistants, streaming video platforms that recommend personalized content, and vehicle navigation systems that suggest best routes in real time to avoid traffic, all examples of artificial intelligence and machine learning in the consumer world. While consumer-facing machine learning often has a narrow focus, enterprise machine learning solutions must cater to many different types of businesses, all of which measure success differently. A consumer can expect Netflix to learn her movie preferences by tracking what she and people similar to her click on, a solution that can be built to be fairly generic. However, enterprise machine learning solutions rarely work as seamlessly right out of the box.
While machine learning itself is nothing new, the speed at which data can now be processed, analyzed and actioned has completely changed the machine-learning game. Readily affordable computing power, the quantity of data available, and algorithms we never thought we could use are now possible. Though the fundamental concept remains the same, machine-learning is now far more sophisticated, efficient and easily deployable – and the potential it offers to revolutionize customer experience is truly exciting. Last year, Facebook's VP of Design thought the TNW Conference main stage was the best she'd ever been on. Harnessing machine learning allows businesses to revolutionize the way we all engage with their store or use their service.
In my software development work, I've spent considerable time figuring out how to integrate other tools and technology to make my solutions better. The last few years, my work has expanded to adding machine learning technology into various software and app solutions. Since it's been something new for myself and my developer team, we've learned a significant number of new things about how it works and the best way to integrate this segment of artificial intelligence (AI) into the tools and platforms we are building. Before I share some of the best practices our developer team has created based on experiences using machine learning, I'd like to give you some reasons to move forward with this technology as part of what you may be developing as a tech entrepreneur. Machine learning uses real-time data to analyze existing information in order to predict a future action and direct the response.