InfoQ Homepage Machine Learning Content on InfoQ
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Monitoring AI with AI
Iskandar Sitdikov discusses a solution, tooling and architecture that allows an ML engineer to be involved in delivery phase and take ownership over deployment and monitoring of ML pipelines.
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Migrating ML from Research to Production
Conrado Silva Miranda shares his experience leveraging research to production settings, presenting the major issues faced by developers and how to establish stable production for research.
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Architectures for Big Scale 2D Imagery
Zbigniew Wojna discusses the development of neural architectures for 2D imagery problems in big scale, including analysis of different upsampling methods and mechanism for text recognition and review.
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Building a Voice Assistant for Enterprise
Manju Vijayakumar talks about Einstein Assistant - an AI Voice assistant for enterprises that enables users to "Talk to Salesforce".
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Reasoning about Uncertainty at Scale
Max Livingston presents a case study of using Bayesian modelling and inference to directly model behavior of aircraft arrivals and departures, focusing on the uncertainty in those predictions.
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Machines Can Learn - a Practical Take on Machine Intelligence Using Spring Cloud Data Flow and TensorFlow
Christian Tzolov showcases how building a complex use-case, such as real-time image recognition or object detection, can be simplified with the help of the Spring Ecosystem and TensorFlow.
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Zero to Production in Five Months @ ThirdLove
Megan Cartwright discusses how ThirdLove built their first machine learning recommendation algorithm that predicts bra size and style.
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Designing Automated Pipelines for Unseen Custom Data
Kevin Moore discusses some challenges in designing automated machine learning pipelines that can deal with custom user data that it has never seen before, as well as some of Salesforce’s solutions.
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Machine Learning Interpretability in the GDPR Era
Gregory Antell explores the definition of interpretability in ML, the trade-offs with complexity and performance, and surveys the major methods used to interpret and explain ML models in the GDPR era
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Nearline Recommendations for Active Communities @LinkedIn
Hema Raghavan focusses on technologies they have built to power LinkedIn’s “People You May Know” product and describes their nearline platform for notification recommendation.
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Human-centric Machine Learning Infrastructure @Netflix
Ville Tuulos discusses the tools Netflix built for the data scientists and some of the challenges and solutions made to create a paved road for machine learning models to production.
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Genetic Programming in the Real World: A Short Overview
Leonardo Trujillo overviews how GP can be used to solve ML tasks intended as a starting point for applied researchers and developers.
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Applied AI & ML conference for software engineers
April 15-17, 2019
San Francisco, California
Discover emerging AI trends, essential tools, and how to apply AI in your software projects.
QCon.ai Tracks
- Deep Learning in Practice
- Papers in Production: Modern CS in the Real World
- Predictive Architectures in the Real World
- AI Meets the Physical World
- Groking Timeseries & Sequential Data
- Solving Software Engineering Problems with Machine Learning
Featured Talk
Applying Deep Learning To Airbnb Search
Machine Learning Engineer @Airbnb
Malay Haldar
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QCon.ai at a glance
- 6 tracks across 2 days
- 1 day of optional workshops
- 35+ speakers from early adopter companies
- Practitioners over evangelists
- Never any hidden marketing
- Peer sharing opportunities
