InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
-
Applying Deep Learning to Airbnb Search
Malay Haldar discusses the work done in applying neural networks at Airbnb to improve the search beyond the results obtained with ML.
/presentations/dl-airbnb/en/mediumimage/Malay-Haldar-m-1559954725887.jpg)
-
People You May Know: Fast Recommendations over Massive Data
Sumit Rangwala and Felix GV present the evolution of PYMK’s architecture, focusing on Gaia, a real-time graph computing capability, and Venice, an online feature store with scoring capability.
/presentations/recommendation-massive-data/en/mediumimage/recommendation-massive-data-m-1559954226481.jpg)
-
Ludwig: A Code-Free Deep Learning Toolbox
Piero Molino introduces Ludwig, a deep learning toolbox that allows to train models and to use them for prediction without the need to write code.
/presentations/ludwig-dl-toolbox/en/mediumimage/piero_molino-h-1559952531778.jpeg)
-
Deep Learning on Microcontrollers
Pete Warden discusses why Deep Learning is a great fit for tiny, cheap devices, what can be built with it, and how to get started.
/presentations/dl-microcontrollers/en/mediumimage/Pete-Warden-m-1559787428228.jpeg)
-
Corda: Looking Forward and Back, Blockchain on a JVM Stack
Carolyne Quinn and Mike Ward discuss Corda, an open source enterprise blockchain, and look at technologies used: Kotlin, JVMs, pluggable consensus, Corda Foundation and SGX,.
/presentations/corda-blockchain-jvm/en/mediumimage/Doibig-1559265778122.JPG)
-
Practical NLP for the Real World
Emmanuel Ameisen discusses examples of how to build practical applications using NLP, diving into data visualization and labelling, as well as model validation.
/presentations/practical-nlp/en/mediumimage/Emmanuel-Ameisen-m-1559813972096.jpg)
-
Federated Learning: Rewards & Challenges of Distributed Private ML
Eric Tramel discusses the basic concepts underlying the federated ML approach, the advantages it brings, as well as the challenges associated with constructing federated solutions.
/presentations/federated-learning-distributed-ml/en/mediumimage/Eric-Tramel-m-1559214121717.jpg)
-
Michelangelo Palette: A Feature Engineering Platform at Uber
Amit Nene and Eric Chen discuss the infrastructure built by Uber for Michelangelo ML Platform that enables a general approach to Feature Engineering across diverse data systems.
/presentations/michelangelo-palette-uber/en/mediumimage/Doibig-1559179009887.JPG)
-
Instrumentation, Observability & Monitoring of Machine Learning Models
Josh Wills discusses the monitoring and visibility needs of machine learning models in order to bridge gaps between ML practitioners and DevOps.
/presentations/instrumentation-observability-monitoring-ml/en/mediumimage/Josh-Wills-m-1559221203140.jpg)
-
Life of a Distributed Graph Database Query
Teon Banek describes the life of a query in Memgraph following the process from reading a query as a character string, through planning and distributed execution of query operations.
/presentations/graph-query-distributed-execution/en/mediumimage/Teonbig-1558927784336.JPG)
-
Privacy: The Last Stand for Fair Algorithms
Katharine Jarmul discusses research related to fair-and-private ML algorithms and privacy-preserving models, showing that caring about privacy can help ensure a better model overall and support ethics
/presentations/privacy-fair-algorithms/en/mediumimage/Katbig-1558709945482.JPG)
-
The Future of Transportation
Anita Sengupta discusses the future of transportation with an eye towards how machine learning and AI will help shape the future.
/presentations/future-transportation/en/mediumimage/Anibig-1558624462271.JPG)