InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
-
Building a Data Pipeline with the Tools You Have - An Orbitz Case Study
Steve Hoffman, Ken Dallmeyer share their experience integrating Hadoop into the existing environment at Orbitz, creating a reusable data pipeline, ingesting, transporting, consuming and storing data.
/presentations/orbitz-big-data/en/mediumimage/Steve.JPG)
-
Weathering the Data Storm
Claudia Perlich discusses privacy-preserving representations, robust high-dimensional modeling, large-scale automated learning systems, transfer learning, and fraud detection.
/presentations/display-advertising-big-data/en/mediumimage/Claudiabig.jpg)
-
Apache Spark Plus Many Other Frameworks: How Spark Fits into the Big Data Landscape
Paco Nathan keynotes on how Spark fits into the big data landscape, describing what other systems work with Spark, and explaining why Spark is needed in the future.
/presentations/apache-spark-future/en/mediumimage/Pacobig.jpg)
-
The Business Value of Big Data Driven by the Internet of Everything
John Zamierowski discusses the business benefits of big data coming from the Internet of Everything, focusing on the "Why" and "How" of big data and current developments in sensor technology.
/presentations/big-data-internet-everything/en/mediumimage/Johnbig.jpg)
-
Scaling Chartbeat from 8 Million Open Browsers to Realtime Analytics and Optimization
Wesley Chow presents Chartbeat's real-time analytics platform and how able to handle the requests in a cost efficient manner using a custom written analytics engine in C and Lua.
/presentations/scale-chartbeat/en/mediumimage/Wesbig.jpg)
-
NoSQL Like There is No Tomorrow
The authors take a deep dive into the history of NoSQL at Amazon.com, from the world of relational databases to the Dynamo days to the world of managed services like DynamoDB.
/presentations/aws-nosql-lessons/en/mediumimage/Swabig.jpg)
-
Practical Machine Learning
Seth Juarez introduces the nuML machine learning library, addressing the clustering issue in .NET applications by focusing on recommendation engines and anomaly detection.
/presentations/numl/en/mediumimage/Sethbig.jpg)
-
Introduction to Data Science
Bryan Nehl makes an introduction to the data science: data formats, ETL tools, NoSQL databases, languages, libraries, techniques and approaches for exploring data and extracting value from it.
/presentations/introduction-data-science/en/mediumimage/Bryanbig.jpg)
-
Machine Learning at Netflix Scale
Aish Fenton discusses Netflix' machine learning algorithms, including distributed Neural Networks on AWS GPUs, providing insight into offline experimentation and online AB testing.
/presentations/machine-learning-netflix-2014/en/mediumimage/Aishbig.jpg)
-
An Introduction to Spring Data
Frank Moley introduces Spring Data and how to use it for applications connected to either RDBMS or NoSQL databases.
/presentations/spring-data-rdbms-nosql/en/mediumimage/Frankbig.jpg)
-
Persistence: A View from Stratosphere
Stefan Edlich discusses big data systems -Spanner, Presto- and the future of data persistence, data analytics, data formats and of NoSQL/NewSQL in general.
/presentations/big-data-persistence-nosql/en/mediumimage/Stefanbig.jpg)
-
Leveraging Big Data for Payment Risk Management
John Canfield discusses the changing payment ecosystem, innovations in mining and organizing unstructured data from many sources, and approaches to deciding for loss minimization and user experience.
/presentations/big-data-payment-risk-management/en/mediumimage/Johnbig.jpg)