InfoQ Homepage Presentations
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Lessons Learned from Remote-First SRE
James McNeil discusses how they have made remote working sustainable at Netlify, practices which can improve hybrid and in-person incident management.
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The Unreasonable Effectiveness of Zero Shot Learning
Roland Meertens shows how one can get started deploying models without requiring any data, discussing foundational models, and examples of them, such as GPT-3 and OpenAI CLIP.
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Designing Event-Driven Architectures Using the AsyncAPI Specification
Fran Mendez discusses event-driven or asynchronous APIs, comparing AsyncAPI with OpenAPI/Swagger, AMQP/MQTT/Kafka with HTTP, and publish/subscribe with request/response.
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Microservices to Async Processing Migration at Scale
Sharma Podila shares from their experience migrating to asynchronous processing at scale, requiring attention to managing data loss, a highly available infrastructure, and elasticity to handle bursts.
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Java 17: the Path, Features, Tips and Tricks Panel
Simon Ritter, Kristen O'Leary and Rory Preddy discuss the path to Java 17 and tips to ease the transition.
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A Standardized, Specification-Driven API Lifecycle
Kin Lane discusses API specifications like OpenAPI and AsyncAPI, and how they have emerged as the way API producers and consumers are engaging across the entire API lifecycle.
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ML Panel: "ML in Production - What's Next?"
The panelists discuss lessons learned with putting ML systems into production, what is working and what is not working, building ML teams, dealing with large datasets, governance and ethics/privacy.
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Full Stack Dart
Chris Swan discusses using a stack of Dart, where Flutter developers can use the same language to build the services behind their apps.
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Machine Learning at the Edge
Katharine Jarmul discusses utilizing new distributed data science and machine learning models, such as federated learning, to learn from data at the edge.
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Architecting for the Edge
The panelists discuss main differences in how one should design and build services when embracing the Edge as part of the system architecture.
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Unified MLOps: Feature Stores and Model Deployment
Monte Zweben proposes a whole new approach to MLOps that allows to scale models without increasing latency by merging a database, a feature store, and machine learning.
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Reproducible Development with Containers
Avdi Grimm describes the future of development, which is already here. Get a tour of a devcontainer, and contrast it with a deployment container.
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