Production Model Deployment Patterns: From REST APIs to Kubernetes Orchestration in Python

After deploying hundreds of ML models to production across startups and enterprises, I’ve learned that model deployment is where most AI projects fail. Not because the models don’t work—but because teams underestimate the engineering complexity of serving predictions reliably at scale. This article shares production-tested deployment patterns from REST APIs to Kubernetes orchestration. 1. The […]

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Production Data Pipelines with Apache Airflow: From DAG Design to Dynamic Task Generation

After 20 years in enterprise data engineering, I’ve implemented Apache Airflow across healthcare, financial services, and cloud-native architectures. This article shares production-tested patterns for building resilient, scalable data pipelines—from DAG design principles to dynamic task generation strategies that handle thousands of workflows. 1. The Fundamentals: Why Airflow Remains the Standard Apache Airflow has become the […]

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Security as Code: Why the Best DevSecOps Teams Treat Vulnerabilities Like Bugs, Not Afterthoughts

The first time I watched a security vulnerability slip through our CI/CD pipeline and make it to production, I felt the same sinking feeling every engineer knows: that moment when you realize the system you trusted has a blind spot. It was 2019, and we had what we thought was a mature DevOps practice. Automated […]

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Observability Practices in AI Engineering: A Complete Guide to LLM Monitoring

Master AI observability with this comprehensive guide. Compare Langfuse, Helicone, LangSmith, and other tools. Learn which metrics matter, how to build evaluation pipelines, and implement production-grade monitoring for LLM applications.

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