InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Designing IoT Data Pipelines for Deep Observability
Shrijeet Paliwal discusses how Tesla deals with large data ingestion and processing, the challenges with IoT data collecting and processing, and how to deal with them.
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Evolving Analytics in the Data Platform
Blanca Garcia-Gil discusses the BBC’s analytics platform architecture, the failure modes they designed for, and the investigation of the new unknowns and how they automated them away.
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Scaling & Optimizing the Training of Predictive Models
Nicholas Mitchell presents the core building blocks of an entire toolchain able to deal with challenges of large amounts of data in an industrial scalable system.
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Using DevEx to Accelerate GraphQL Federation Adoption @Netflix
Paul Bakker and Kavitha Srinivasan discuss how they made certain Build vs Buy (open source) trade-offs and the socio-technical aspects of working with many teams on a single shared schema.
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Building Latency Sensitive User Facing Analytics via Apache Pinot
Chinmay Soman discusses how LinkedIn, Uber and other companies managed to have low latency for analytical database queries in spite of high throughput.
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A Functional Tour of Automatic Differentiation
Oliver Strickson discusses automatic differentiation, a family of algorithms for taking derivatives of functions implemented by computer programs, offering the ability to compute gradients of values.
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BERT for Sentiment Analysis on Sustainability Reporting
Susanne Groothuis discusses how KPMG created a custom sentiment analysis model capable of detecting subtleties, and provides them with a metric indicating the balance of a report.
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Applying Machine Learning to Financial Payments
Tamsin Crossland discusses how ML can be applied to Payments to respond rapidly to known and emerging patterns of fraud, and to detect patterns of fraud that may not otherwise be identified.
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The Fast Track to AI with JavaScript and Serverless
Peter Elger explores how to get started building AI enabled platforms and services using full stack JavaScript and Serverless technologies.
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Is Machine Learning the Right Tool?
Brian Korzynski discusses when and where using machine learning will fit within projects.
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Visual Intro to Machine Learning and Deep Learning
Jay Alammar offers a mental map of Machine Learning prediction models and how to apply them to real-world problems with many examples from existing businesses and products.
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From Mainframe to Microservices with Pivotal Platform and Kafka: Bridging the Data Divide
Dmitry Milman and Ankur Kaneria showcase how Pivotal and Apache Kafka are leveraged within Express Scripts’ transformation from mainframe to a microservices-based ecosystem, ensuring data integrity.
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