InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Building & Operating High-Fidelity Data Streams
Sid Anand discusses building high-fidelity nearline data streams as a service within a lean team.
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Data Pipelines & Data Mesh: Where We Are and What the Future Looks Like
Zhamak Dehghani, Tareq Abedrabbo and Jacek Laskowski discuss the current challenges for building Modern Data Pipelines and applying Data Mesh in the real world, what the future looks like, and tools.
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Federated GraphQL to Solve Service Sprawl at Major League Baseball
Olessya Medvedeva and Matt Oliver discuss how they have begun to implement a Federated GraphQL architecture to solve the issue of service discovery, sprawl and ultimately getting the data needed.
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Experimenting with WASM for Future Audience Experiences in BBC iPlayer
Tim Pearce discusses how they used WebAssembly to deploy their iPlayer across various web browsers, what advantages this approach had and how they intend to use WebAssembly outside the browser.
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Panel: Future of Language Support for ML
Jendrik Jördening, Irene Dea, Alanna Tempest take a look at the state of the art of ML/AI development and how advances in language technology (specifically differentiable programming langs) can help.
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How Do You Distribute Your Database over Hundreds of Edge Locations?
Erwin van der Koogh explains a new model that Cloudflare has developed to distribute a database over hundreds of locations, and where it could go next.
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Panel: Challenges & Opportunities of the Modern Financial Institutions
Lucas Cavalcanti, Dio Rettori, and Camilla Crispim discuss the challenges and opportunities of modern financial institutions.
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Robust Foundation for Data Pipelines at Scale - Lessons from Netflix
Jun He and Harrington Joseph share their experiences of building and operating the orchestration platform for Netflix’s big data ecosystem.
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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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