InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Data Mesh: an Architectural Deep Dive
Zhamak Dehghani introduces the architecture of new Data Mesh concepts such as data products, as well as the planes of the data platform in support of computational governance and distribution.
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From Batch to Streams: Building Value from Data In-Motion
Ricardo Ferreira discusses the risks of designing siloed-based systems and how streaming data architectures can become a solution to address competitiveness.
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Designing Better ML Systems: Learnings from Netflix
Savin Goyal shares lessons learned by Netflix building their ML infrastructure, and some of the tradeoffs to consider when designing or buying a machine learning system.
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Data-driven Development in the Automotive Field
Toshika Srivastava offers insight into how they in the automotive field are developing products with data and what their challenges are.
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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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