InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Simplifying Real-Time ML Pipelines with Quix Streams
Tomáš Neubauer discusses Quix Streams, an open-source Python library that helps data scientists and ML engineers to build real-time ML pipelines.
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Improve Feature Freshness in Large Scale ML Data Processing
Zhongliang Liang covers the impact of feature freshness on model performance, discussing various strategies and techniques that can be used to improve feature freshness.
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The Rise of the Serverless Data Architectures
Gwen Shapira explores the implications of serverless workloads on the design of data stores, and the evolution of data architectures toward more flexible scalability.
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Amazon DynamoDB Distributed Transactions at Scale
Akshat Vig explains how transactions were added to Amazon DynamoDB using a timestamp-based ordering protocol to achieve low latency for both transactional and non-transactional operations.
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Responsible AI: from Principle to Practice!
Mehrnoosh Sameki discusses Responsible AI best practices to apply in a machine learning lifecycle and shares open source tools to incorporate to implement Responsible AI in practice.
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Needle in a 930M Member Haystack: People Search AI @LinkedIn
Mathew Teoh explores how LinkedIn's People Search system uses ML to surface the right person that you're looking for.
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PostgresML: Leveraging Postgres as a Vector Database for AI
Montana Low provides an understanding of how Postgres can be used as a vector database for AI and how it can be integrated into your existing application stack.
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Introducing the Hendrix ML Platform: an Evolution of Spotify’s ML Infrastructure
Divita Vohra and Mike Seid discuss Spotify’s newly branded platform, and share insights gained from a five-year journey building ML infrastructure.
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ChatGPT and AI: What's Next in Large Language Model (LLM) Architectures
The panelists discuss what's next in Large Language Model (LLM) architectures used in tools like ChatGPT and how these tools will further disrupt the AI/ML space.
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Strategy & Principles to Scale and Evolve MLOps @DoorDash
Hien Luu shares their approach to MLOps, and the strategy and principles that have helped them to scale and evolve their platform to support hundreds of models and billions of predictions per day.
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Declarative Machine Learning: a Flexible, Modular and Scalable Approach for Building Production ML Models
Shreya Rajpal discusses declarative ML systems, and how they address key issues that help shorten the time taken to bring ML models to production.
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Laying the Foundations for a Kappa Architecture - the Yellow Brick Road
Sherin Thomas discusses strategies to evolve Data Infrastructure to enable Kappa architecture in an organization.
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