InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Machine Intelligence at Google Scale
Guillaume LaForge presents pre-trained ML services such as Cloud Vision API and Speech API that works without any training, introducing Cloud AutoML.
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Fuelling the AI Revolution with Gaming
Alison Lowndes talks about the HW & SW that comprise NVIDIA's GPU computing platform for AI, across PC to data center, cloud to edge, training to inference.
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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.
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AI Panel
Panelists attempt to demystify AI and answer questions from the public.
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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.
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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.
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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.
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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.
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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.
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Introducing FlureeDB, The World's First ACID-Compliant Blockchain Database
Brian Platz introduces FlureeDB, a graph-style database for building blockchain applications.
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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.
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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.
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