InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Speed of Apache Pinot at the Cost of Cloud Object Storage with Tiered Storage
Neha Pawar discusses how to query data on the cloud directly with sub-seconds latencies, diving into data fetch and optimization strategies, challenges faced and learnings.
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Real-Time Machine Learning: Architecture and Challenges
Chip Huyen discusses the value of fresh data as well as different types of architecture and challenges of online prediction.
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AI Bias and Sustainability
Leslie Miley discusses how the road to ubiquitous AI is clouded by the dangers of the inherent bias in Large Language Models and the increased CO2 emissions that come with deployment at scale.
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A Bicycle for the (AI) Mind: GPT-4 + Tools
Sherwin Wu and Atty Eleti discuss how to use the OpenAI API to integrate large language models into your application, and extend GPT’s capabilities by connecting it to the external world via APIs.
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A New Era for Database Design with TigerBeetle
Joran Dirk Greef discusses pivotal moments in database design and how they influenced the design decisions for TigerBeetle, a distributed financial accounting database.
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Streaming from Apache Iceberg - Building Low-Latency and Cost-Effective Data Pipelines
Steven Wu discusses the design of the Flink Iceberg, comparing the Kafka and Iceberg sources for streaming and how the Iceberg streaming source can power many common stream processing use cases.
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Azure Cosmos DB: Low Latency and High Availability at Planet Scale
Mei-Chin Tsai and Vinod Sridharan discuss the internal architecture of Azure Cosmos DB and how it achieves high availability, low latency, and scalability.
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Amazon DynamoDB: Evolution of a Hyperscale Cloud Database Service
Akshat Vig presents Amazon’s experience operating DynamoDB at scale and how the architecture continues to evolve to meet the ever-increasing demands of customer workloads.
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Unraveling Techno-Solutionism: How I Fell out of Love with “Ethical” Machine Learning
Katharine Jarmul confronts techno-solutionism, exploring ethical machine learning, which eventually led her to specialize in data privacy.
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Operationalizing Responsible AI in Practice
Mehrnoosh Sameki discusses approaches to responsible AI and demonstrates how open source and cloud integrated ML help data scientists and developers to understand and improve ML models better.
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Orchestrating Hybrid Workflows with Apache Airflow
Ricardo Sueiras discusses how to leverage Apache Airflow to orchestrate a workflow using data sources inside and outside the cloud.
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Open Machine Learning: ML Trends in Open Science and Open Source
Omar Sanseviero discusses the trends in the ML ecosystem for Open Science and Open Source, the power of creating interactive demos using Open Source libraries and BigScience.
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