InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
-
Real-Time Data Analysis and ML for Fraud Prevention
Mikhail Kourjanski addresses the architectural approach towards the PayPal internally built real-time service platform, which delivers performance and quality of decisions.
/presentations/paypal-data-analysis-ml-fraud/en/mediumimage/Mikbig-1527943584688.JPG)
-
End-to-End ML without a Data Scientist
Holden Karau discusses how to train models, and how to serve them, including basic validation techniques, A/B tests, and the importance of keeping models up-to-date.
/presentations/ml-models-ab-tests/en/mediumimage/End-to-End-ML-without-Data-Scientist-ribbon-1530266022644.jpg)
-
Deep Learning for Science
Prabhat discusses machine learning's impact on climatology, astronomy, cosmology, neuroscience, genomics, and high-energy physics, and the future of AI in powering scientific discoveries.
/presentations/deep-learning-science/en/mediumimage/Prabig-1527731821296.JPG)
-
Liquidity Modeling in Real Estate Using Survival Analysis
Xinlu Huang and David Lundgren discuss hazard and survival modeling, metrics, and data censoring, describing how Opendoor uses these models to estimate holding times for homes and mitigate risk.
/presentations/liquidity-modeling-risk/en/mediumimage/Doibig-1527731396587.JPG)
-
CRDTs and the Quest for Distributed Consistency
Martin Kleppmann explores how to ensure data consistency in distributed systems, especially in systems that don't have an authoritative leader, and peer-to-peer communication.
/presentations/crdt-distributed-consistency/en/mediumimage/Marbig-1527380291967.JPG)
-
Introducing FlureeDB, The World's First ACID-Compliant Blockchain Database
Brian Platz introduces FlureeDB, a graph-style database for building blockchain applications.
/presentations/flureedb/en/mediumimage/Bribig-1527378914180.JPG)
-
Data Pipelines for Real-Time Fraud Prevention at Scale
Mikhail Kourjanski discusses the architecture of PayPal’s data service which combines a Big Data approach with providing data in real time for decision making in fraud detection.
/presentations/paypal-data-service-fraud/en/mediumimage/Mikbig-1527121529188.JPG)
-
pDB: Scalable Prediction Infrastructure with Precision and Provenance
Balaji Rengarajan describes the platform built on the Celect’s pDB framework, providing multiple use cases such as online personalization, document classification, and geospatial anomaly detection.
/presentations/celect-pdb/en/mediumimage/Balbig-1527086103910.JPG)
-
Self-Racing Using Deep Neural Networks: Lap 2
Jendrik Joerdening and Anthony Navarro discuss how a team of Udacity students used neural networks to teach a car to drive by itself around a track in two days.
/presentations/udacity-self-driving-ai/en/mediumimage/doibig-1527085651698.JPG)
-
The Black Swan of Perfectly Interpretable Models
Mayukh Bhaowal, Leah McGuire discuss how Salesforce Einstein made ML more transparent and less of a black box, and how they managed to drive wider adoption of ML.
/presentations/salesforce-einstein-ml/en/mediumimage/Doibig-1527036251434.JPG)
-
Counting is Hard: Probabilistic Algorithms for View Counting at Reddit
Krishnan Chandra explains the challenges of building a view counting system at scale, and how Reddit used probabilistic counting algorithms to make scaling easier.
/presentations/algorithms-counting-reddit/en/mediumimage/Kribig-1526475749422.JPG)
-
Developing Data and ML Pipelines at Stitch Fix
Jeff Magnusson discusses thoughts and guidelines on how Stitch Fix develops, schedules, and maintains their data and ML pipelines.
/presentations/data-ml-pipelines-stitchfix/en/mediumimage/Jefbig-1526475400409.JPG)