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Best practices for ML API design
Designing APIs for machine learning systems requires combining software engineering rigor with data science insight. This article explores best practices for building scalable, maintainable, and reproducible ML APIs, covering versioning, schema management, performance, and lifecycle integration used by top tech companies.
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Topics Everyone Is Talking About No280
IDEsaster: A New Class of Vulnerabilities in AI-Powered IDEs • KOllector: Publishing and Syncing KOReader Highlights with Flask • Defeating Prompt Injections by Design • Measuring Agents in Production Survey Paper • Bag of Words, Have Mercy on Us
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Topics Everyone Is Talking About No223
Claude Opus 4.5 Anthropics Next Leap in Advanced Language Models • Claude Advanced Tool Use Smarter and Scalable AI Tool Orchestration • AI and the Classroom Karpathy on the Future of Learning • Modeling Agent Systems with Erlang A 2004 Vision of Distributed Intelligence • Inside Powersets Natural Language Search Lessons from an Early Semantic…