We will explore using BigML to add machine learning integration into your apps via the cloud..
BigML.io is a REST-style API for creating and managing BigML resources programmatically. Using BigML.io you can create, retrieve, update and delete Sources, Datasets, Models, Ensembles, Predictions, and Evaluations using standard HTTP methods.
BigML.io gives you: Secure programmatic access to all your BigML resources. Fully white-box access to your datasets and models. Asynchronous creation of datasets, models, ensembles and evaluations. Near real-time predictions.
With the rapid progress of urbanization and civilization on earth, urban computing is emerging as a concept where every sensor, device, person, vehicle, building, and street in the urban areas can be used as a component to probe city dynamics to further enable city-wide computing for serving people and their cities. Urban computing aims to enhance both human life and urban environment smartly through a recurrent process of sensing, mining, understanding, and improving. Urban computing also aims to deeply understand the nature and sciences behind the phenomenon occurring in urban spaces, using a variety of heterogeneous data sources, such as traffic flows, human mobility, geographic and map data, environment, energy consumption, populations, and economics, etc. Recently, real-world data reflecting city dynamics becomes widely available, including, e.g., users’ mobile phone signal, GPS traces of vehicles and people, ticketing data in public transportation systems, user-generated content (like tweets, micro-blog, check-ins, photos), data from transportation sensor networks (camera and loop sensors) and environment sensor networks (temperature and air quality), as well as data from the Internet of Things. As a result, we are ready to carry out real urban computing activities that lead to better and smarter cities. By better sensing and understanding the city dynamics we are more likely to design effective strategies and intelligent systems for improving urban lives.