Ellissa Verseput is a Data Scientist at Quby, a leading company offering data driven home services technology. In this role she is responsible for developing end-to-end data driven services to enable commodity suppliers such as utilities and insurance companies to play a dominant role in the home services domain. Thanks to her previous experience in software and data engineering, she enjoys robust productionizing and building bridges between data science and her colleagues from other disciplines at Quby. Ellissa has a master’s degree in Econometrics & Operations Research and has been working in the IT & data science field since 2016.
Quby is a leading company offering data driven home services technology across European markets, known for creating the in-home display and smart thermostat Toon. We enable our partners to take on a leading role in the home services domain, by offering data driven home services. Our services enable users to control and monitor their homes using both an in-home display and app.
As a data driven company, we use AI and machine learning, backed by Apache Spark, to generate actionable insights for all our end users. Via our IoT devices we have access to Europe's largest energy dataset, petabytes in scale and growing exponentially. This unique dataset enables us to introduce new data driven services, with a particular focus on homes with smart meter installations.
In this talk, Ellissa will describe how machine learning is implemented on the Quby platform and will show multiple use cases backed by high-resolution IoT data. We'll take a look at super resolution techniques for time series data, where using detailed high-resolution energy data is used to show personalized energy insights for users where only limited low-resolution energy data is available. We'll show how ML algorithms offer the possibility for non-intrusive monitoring of elderly patients.
Ellissa will share the experiences from the Data Science and Data Engineering teams at Quby with bringing these data science algorithms from R&D to production using Databricks and the lessons learned in offering these services to hundreds of thousands of users on a daily basis.