InfoQ Homepage Predictions Content on InfoQ
Presentations
RSS Feed-
Real-Time Machine Learning: Architecture and Challenges
Chip Huyen discusses the value of fresh data as well as different types of architecture and challenges of online prediction.
/presentations/ml-arch-challenges/en/mediumimage/chip-huyen-medium-1691571717978.jpg)
-
Ludwig: A Code-Free Deep Learning Toolbox
Piero Molino introduces Ludwig, a deep learning toolbox that allows to train models and to use them for prediction without the need to write code.
/presentations/ludwig-dl-toolbox/en/mediumimage/piero_molino-h-1559952531778.jpeg)
-
Reasoning about Uncertainty at Scale
Max Livingston presents a case study of using Bayesian modelling and inference to directly model behavior of aircraft arrivals and departures, focusing on the uncertainty in those predictions.
/presentations/flight-bayesian-prediction/en/mediumimage/Maxbig-1548038456988.JPG)
-
Your Project Behaves Like a Hurricane. Forecast It Like One
Daniel Vacanti discusses using techniques like Monte Carlo Simulation and Continuous Forecasting to make projects more predictable.
/presentations/predictable-projects-continuous-forecasting/en/mediumimage/daniel-vacanti-m-1536754379956.jpeg)
-
Engineering Systems for Real-Time Predictions @DoorDash
Raghav Ramesh presents DoorDash’s thoughts on how to structure ML systems in production to enable robust and wide-scale deployment of ML, and shares best practices in designing engineering tooling.
/presentations/doordash-real-time-predictions/en/mediumimage/Ragbig-1534992102793.JPG)
-
Tools to Put Deep Learning Models in Production
Sahil Dua discusses how Booking.com supports data scientists by making it easy to put their models in production, and how they optimize their model prediction infrastructure for latency or throughput.
/presentations/booking-dl-models/en/mediumimage/Sabig-1527954363251.JPG)
-
Our Concurrent Past; Our Distributed Future
Joe Duffy talks about the concurrency's explosion onto the mainstream over the past 15 years and attempts to predict what lies ahead for distributed programming, from now til 15 years into the future.
/presentations/concurrency-distributed-computing/en/mediumimage/joebig.JPG)
-
Bringing Machine Learning to Every Corner of Your Business
Danny Lange presents Uber’s Machine Learning service that can perform functions such as ETA, fraud detection, churn prediction, forecasting demand, and much more.
/presentations/uber-ml/en/mediumimage/Danbig.JPG)
-
Predicting the Future: Surprising Revelations trom Truly Big Data
Pushpraj Shukla discusses how Microsoft Bing predicts the future based on aggregate human behavior using one of the largest scale data sets, and recent progress in large scale deep learnt models.
/presentations/bing-predict-future/en/mediumimage/Pubig.JPG)
-
Implementing a Highly Scalable Stock Prediction System with R, GemFire and Spring XD
William Markito Oliveira and Fred Melo discuss the architecture and implementation details of a stock prediction solution built entirely on top of open source code and some R and a web interface.
/presentations/r-gemfire-spring-xd/en/mediumimage/Doibig.JPG)
-
The Big Data Revolution
Claudia Perlich keynotes on M6D’s approach to Big Data, using data granularity to build predictive models used for user targeting, bid optimization and fraud detection.
/presentations/m6d-big-data/en/mediumimage/Claudiabig.jpg)
-
A Brief History of the (Java) World and a Peek Forward
Neal Gafter reviews the long history of Java from its inception to the present and makes an incursion into what he thinks will be a great future and guessing what might come in Java SE 9+ after 2014.
/presentations/A-Brief-History-of-the-Java-World/en/smallimage/Neal.JPG)