InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
-
Large-Scale Stream Processing with Apache Kafka
Neha Narkhede explains how Apache Kafka was designed to support capturing and processing distributed data streams by building up the basic primitives needed for a stream processing system.
/presentations/apache-kafka-streams/en/mediumimage/Nebig.JPG)
-
Online Data Mining and Machine Learning
Edo Liberty presents some basic concepts and an introduction to the subfields of machine learning and data mining.
/presentations/data-mining-machine-learning/en/mediumimage/Edobig.JPG)
-
Introducing Apache Ignite
Christos Erotocritou introduces Apache Ignite, discussing how it is used to solve some of the most demanding scalability and performance challenges. He covers typical use cases and examples.
/presentations/apache-ignite/en/mediumimage/Chbig.JPG)
-
Building a Predictive Intelligence Engine
Viral Bajaria explains a formula for reaching the B2B buyer early in the sales cycle by tying together billions of rows of customer data and overlaying predictive intelligence technology.
/presentations/predictive-intelligence-engine/en/mediumimage/Vibig.JPG)
-
The Future of Data Science
The panelists discuss some of the trends in data science today, the job of a data scientist, the tools and other related issues.
/presentations/panel-data-science/en/mediumimage/Panelbig.jpg)
-
APIs, Spreadsheets & Drinking Fountains: Using Open Data in Real Life
Shelby Switzer discusses success stories and failures of using the public data provided by governments, along with techniques for making such data usable.
/presentations/government-open-data-api/en/mediumimage/Shebig.JPG)
-
Detecting Anomalies in Streaming Data, Evaluating Algorithms for Real-World Use
Alexander Lavin introduces the Numenta Anomaly Benchmark (NAB), a framework for evaluating anomaly detection algorithms on streaming data.
/presentations/numenta-anomaly-benchmark/en/mediumimage/Albig.JPG)
-
GoshawkDB: Making Time with Vector Clocks
Matthew Sackman discusses dependencies between transactions, how to capture these with Vector Clocks, how to treat Vector Clocks as a CRDT, and how GoshawkDB uses them for a distributed data store.
/presentations/goshawkdb/en/mediumimage/MAtbig.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)
-
Staying in Sync: from Transactions to Streams
Martin Kleppmann explores using event streams and Kafka for keeping data in sync across heterogeneous systems, and compares this approach to distributed transactions.
/presentations/event-streams-kafka/en/mediumimage/Marbig.JPG)
-
Netflix Keystone - How We Built a 700B/day Stream Processing Cloud Platform in a Year
Peter Bakas presents in detail how Netflix has used Kafka, Samza, Docker, and Linux to implement a multi-tenant pipeline processing 700B events/day in the Amazon AWS cloud.
/presentations/netflix-keystone/en/mediumimage/Pebig.JPG)
-
Hunting Criminals with Hybrid Analytics
David Talby demos using Python libraries to build a ML model for fraud detection, scaling it up to billions of events using Spark, and what it took to make the system perform and ready for production.
/presentations/python-fraud-detection/en/mediumimage/Dabig.JPG)