InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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When Models Go Rogue: Hard Earned Lessons on Using Machine Learning in Production
David Talby summarizes best practices & lessons learned in ML, based on nearly a decade of experience building & operating ML systems at Fortune 500 companies across several industries.
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Causal Inference in Data Science
Amit Sharma discusses the value of counterfactual reasoning and causal inference, demonstrating that relying on predictive modeling based on correlations can be counterproductive.
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Orchestrating Chaos: Applying Database Research in the Wild
Peter Alvaro describes LDFI’s (Lineage-driven Fault Injection) theoretical roots in database research, presenting early results from the field and opportunities for near and long-term future research.
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Scaling with Apache Spark
Holden Karau looks at Apache Spark from a performance/scaling point of view and what’s needed to handle large datasets.
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Large Scale Machine Learning for Payment Fraud Prevention
Venkatesh Ramanathan presents how advanced machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention.
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AI as a Service at Scale: Retail Case Study
Eldar Sadikov discusses emerging applications of AI in retail, illustrating how Jetlore's machine learning rank technology is currently utilized to power millions of consumer experiences every day.
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Serverless Design Patterns with AWS Lambda: Big Data with Little Effort
Tim Wagner discusses Big Data on serverless, showing working examples and how to set up a CI/CD pipeline, demonstrating AWS Lambda with the Serverless Application Model (SAM).
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A Series of Unfortunate Container Events @Netflix
Amit Joshi and Andrew Spyker talk about Project Titus, Netflix's container runtime on top of Amazon EC2.
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Survival of the Fittest - Streaming Architectures
Michael Hansen talks about the core principles that will stand the tests of streaming evolution, the potential pitfalls that we may stumble over on our path to streaming and how to avoid these.
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Streaming for Personalization Datasets at Netflix
Shriya Arora discusses challenges faced with stream processing unbounded datasets, comparing microbatch with event-based approaches using Spark and Flink.
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Build a Better Monster: Morality, Machine Learning and Mass Surveillance
Maciej Ceglowski wonders what tech companies can do to reduce the amount of data collected, closing the path to mass surveillance and bringing some morality in using ML with this data.
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AI in Medicine
Anthony Chang presents the past of AI in medicine, the current development status, and what to expect from the future.
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