InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Migrating from Big Data Architecture to Spring Cloud
Lenny Jaramillo discusses how Northern Trust migrated to PCF, highlighting how this helped them accelerate the delivery of functionality to their customers.
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Crisis to Calm: Story of Data Validation @ Netflix
Lavanya Kanchanapalli discusses safe data propagation at Netflix, circuit breakers, data canaries and staggered rollout effective, and efficient validations via sharing data and isolating change.
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The Right Amount of Trust for AI
Chris Butler discusses the building blocks of AI from a product/design perspective, what trust is, how trust is gained and lost, and techniques one can use to build trusted AI products.
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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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The Future of AI
The panelists discuss the future of artificial intelligence.
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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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Solving Business Problems with AI
The panelists discuss using AI to solve business problems.
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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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Women in AI & Blockchain
The panelists discuss the role women can play in AI and blockchain technologies.
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AI & Blockchain from an Investment Perspective
The panelists discuss building AI and blockchain systems from an investment perspective.
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AI for Software Testing with Deep Learning: Is It Possible?
Emerson Bertolo discusses lessons learned when using pre-trained Convolutional Neural Networks (CNN) models, Image Detection APIs and CNN's built from scratch for this purpose.
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