Practical events dedicated to Predictive Technology
We run two types of events:
- PAPIs is the International conference on predictive applications and APIs, taking place annually. It is the premier forum for the presentation of new machine learning APIs, techniques, architectures, and tools to build predictive applications. It brings together practitioners from industry, government and academia to present new developments, identify new needs and trends, and discuss the challenges of building real-world predictive applications. PAPIs' audience includes developers, data scientists and researchers, but also non-technical types.
- PAPIs Connect is a series of events that complement the annual PAPIs conference and that are more business-focused. They are targeted at decision makers and developers who are interested in real-world Machine Learning and Predictive Apps.
About our presentations:
- PAPIs runs a call for proposals every year, which are reviewed rigorously by a committee of experts and thought leaders in machine learning, data science and predictive analytics. The selected proposals make up most of the conference program.
- PAPIs Connect functions slightly differently with most presentations being from invited speakers.
- None of our events' sessions can be bought — apart from short product showcases which are clearly labeled and grouped together.
Predictive Apps were initially defined by Forrester as apps that provide the right functionality and content at the right time, for the right person, by continuously learning about them and predicting what they’ll need. More generally, they are apps that embed predictive capabilities and they are being embraced by diverse industries such as Automotive, Banking, Insurance, Life Sciences, Manufacturing, Energy, Retail, Telecommunication and Utilities, to solve a myriad of use cases: predictive maintenance, risk analysis, claim prediction, diagnosis, personalized recommendations, lead scoring, churn prevention, etc.
As we are collecting more and more data about what people do, we are also seeing a growing number of Predictive APIs that are making it easier to apply Machine Learning to that data — and thus to create Predictive Apps. These APIs abstract away some of the complexities of creating and deploying predictive models. They make machine learning more accessible to developers. They also allow them to spend more time on user experience, getting data, experimenting and delivering value from data