Tag: Data Science
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Empirical comparison of algorithms
This in-depth article explores empirical benchmarking of algorithms in 2025, highlighting advanced statistical rigor, reproducibility techniques, and modern tooling. It includes examples from sorting and machine learning domains, code samples, pseudographic visualizations, and insights into industry-standard frameworks like Ray, Spark, and MLPerf for real-world performance evaluation.
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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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Intro to model evaluation metrics
Understanding model evaluation metrics is essential for every machine learning practitioner. This post introduces key concepts such as accuracy, precision, recall, F1-score, and more—explaining when and why to use each. It also highlights modern metrics for generative and fair AI systems, and shows practical examples using popular libraries like scikit-learn and PyTorch Lightning.
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Intro to model evaluation metrics
Learn the fundamentals of model evaluation metrics in machine learning, including accuracy, precision, recall, F1-score, and beyond. This beginner-friendly guide covers classification, regression, and generative model metrics, along with modern fairness tools and practical examples using scikit-learn and PyTorch.
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Best practices for robust feature pipelines
Building robust feature pipelines is essential for maintaining accuracy, scalability, and reliability in modern machine learning systems. This post explores engineering best practices, tools, and architectures that leading companies use to ensure feature pipelines remain maintainable, testable, and resilient from development to production.