Technical Briefs
Curated insights on ML systems architecture, operational patterns, and the art of building production-grade intelligence.
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The Case for Explainability-First Model Design
Why starting from interpretability constraints produces more robust production models. A framework for encoding compliance and audit requirements directly into the training objective.
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MLOps at Institutional Scale: Beyond CI/CD
Model registries, data versioning, and automated retraining loops—the infrastructure patterns that separate prototype shops from production-grade ML organizations.
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Agentic Architectures: Guardrails and Observability
Designing multi-agent systems with verifiable execution traces. How to engineer controllability without sacrificing autonomy in high-stakes decision pipelines.
Latency Budgets for Real-Time Inference Pipelines
Quantifying the tradeoffs between model complexity, serving cost, and response time. Practical heuristics for choosing between batch and streaming inference.