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vinod yendluri
Mid-level MLOps Engineer specializing in production machine learning systems
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About
Built an end-to-end churn prediction platform at Freddi's Flowers spanning Spark ETL on AWS, model serving, monitoring, and a stakeholder-facing dashboard. Stands out for combining MLOps rigor with product thinking—adding explainability, action-oriented workflows, and config-driven multi-tenant architecture while improving latency and automating drift response.
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Key Strengths
- end-to-end ownership of ML platforms from ETL to API to stakeholder dashboard
- strong cross-layer debugging rooted in data quality diagnosis
- improved inference latency by 35% by moving feature computation upstream
- built automated model monitoring and retraining workflows
- translates model output into actionable UX for business users
- iterative shipping under ambiguity with smallest useful increment mindset
- pragmatic abstraction of reusable feature engineering APIs
- designed config-driven multi-tenant ML/dashboard systems with guardrails
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