InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
-
Data Science for Hire Ed
Gloria Lau describes some of the products built for the higher education sector, the data standardization process, determining school similarity and identifying notable alumni.
/presentations/data-analysis-hiring/en/mediumimage/Gloriabig.jpg)
-
Machine Learning & Recommender Systems at Netflix Scale
Xavier Amatriain discusses the machine learning algorithms and architecture behind Netflix' recommender systems, offline experiments and online A/B testing.
/presentations/machine-learning-netflix/en/mediumimage/Xavierbig.jpg)
-
R for Big Data
Indrajit Roy presents HP Labs’ attempts at scaling R to efficiently perform distributed machine learning and graph processing on industrial-scale data sets.
/presentations/r-big-data/en/mediumimage/Roybig.jpg)
-
Deploying Machine Learning and Data Science at Scale
Nick Kolegraff discusses common problems and architecture to support all the phases of data science and how to start a data science initiative, sharing lessons from Accenture, Best Buy, and Rackspace.
/presentations/data-science-scale/en/mediumimage/Nikbig.jpg)
-
Working with Databases and Groovy
Paul King presents working with databases in Groovy, covering datasets, GMongo, Neo4J, raw JDBC, Groovy-SQL, CRUD, Hibernate, caching, Spring Data technologies, etc.
/presentations/groovy-db/en/mediumimage/Paulbig.jpg)
-
Functional Programming for Optimization Problems with City of Palo Alto Open Data
Paco Nathan reviews an example data analysis application written in Cascalog used for a recommender system based on City of Palo Alto Open Data.
/presentations/functional-programming-data-analysis/en/mediumimage/Pacobig.jpg)
-
Add ALL the Things: Abstract Algebra Meets Analytics
Avi Bryant discusses how the laws of group theory provide a useful codification of the practical lessons of building efficient distributed and real-time aggregation systems.
/presentations/abstract-algebra-analytics/en/mediumimage/Avibig.jpg)
-
Big Data Platform as a Service at Netflix
Jeff Magnusson details some of Netflix' key services: Franklin, Sting and Lipstick.
/presentations/netflix-big-data-paas/en/mediumimage/Jeffbig.jpg)
-
Stream Processing: Philosophy, Concepts, and Technologies
Dan Frank discusses stream data processing and introduces NSQ – Bitly’s open source queuing system – and other new technologies used for communication between streaming programs.
/presentations/data-streaming-nsq/en/mediumimage/Danbig.jpg)
-
"Big Data" Agile Analytics
Ken Collier discusses Agile Analytics, a combination of sophisticated analytics techniques, lean learning principles, agile delivery methods, and "big data" technologies.
/presentations/big-data-agile-analytics/en/mediumimage/Kenbig.jpg)
-
High Speed Smart Data Ingest into Hadoop
Oleg Zhurakousky discusses architectural tradeoffs and alternative implementations of real-time high speed data ingest into Hadoop.
/presentations/real-time-hadoop/en/mediumimage/Olegbig.jpg)
-
Making the Internet a Better Place: Scaling AppNexus
Mike Nolet shares lessons learned scaling AppNexus and architectural details of their system processing 30TB/day: Hadoop, DNS built in GSLB and Keepalived, and real-time data streaming built in C.
/presentations/appnexus-architecture-scalability/en/mediumimage/Mikebig.jpg)