ML Engineering bridges model development and production deployment. These posts cover benchmark-driven development, evaluation pipelines, and the engineering practices that turn experimental models into reliable production services with measurable performance guarantees.
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How a single sprint of specialty-rule work — guided by a benchmark that wasn't afraid to print embarrassing numbers — turned a 'demo respiratory differential' into a five-condition rule-based diagnostic engine.
All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.