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Introduction to ETL and ELT patterns
ETL and ELT are core data integration patterns that define how organizations move, transform, and analyze information. This post introduces both approaches, their architectures, trade-offs, and modern tooling, helping data engineers understand when to apply each and how to align them with modern cloud-native practices.
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Using timeit and perf to benchmark Python code
This guide explores the practical use of Python’s built-in timeit module and the powerful perf library for accurate benchmarking. Learn how to perform reproducible, statistically robust performance testing using both tools, interpret their results, and integrate them into modern engineering workflows.
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Expert: async and GPU optimization patterns
This post explores advanced techniques for asynchronous execution and GPU optimization in Python and CUDA. It covers multi-stream concurrency, kernel scheduling, distributed training, and real-world optimization case studies to help expert engineers maximize performance and efficiency.
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Tools: Kubeflow, Vertex AI, MLflow Projects
Kubeflow, Vertex AI, and MLflow Projects have become essential in modern MLOps pipelines. This post compares their architectures, orchestration models, and trade-offs to help engineers choose the right tool for scalable machine learning workflows.
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Best practices: responsible data handling and transparency
Responsible AI demands transparency, fairness, and privacy from the ground up. This post explores how engineering teams can build systems that are accountable and explainable, using modern tools and governance structures that align with global AI ethics standards.
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