Tag: Data Science
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Best practices for feature importance ranking
Feature importance ranking is central to explainable machine learning. This guide explores modern post-2024 best practices, including model-based, permutation, and SHAP methods, with code examples and interpretability tips. Learn how leading teams integrate explainability into CI/CD workflows for reliable, transparent, and ethical AI.
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Empirical: LSTM vs Prophet vs ARIMA
This empirical deep dive compares LSTM, Prophet, and ARIMAâthree dominant paradigms in time series forecastingâacross accuracy, interpretability, and computational trade-offs. Drawing from post-2024 experiments, the post explores when each model shines, how they scale in production, and emerging hybrid trends for data scientists.
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Intro to scaling ML inference
Scaling machine learning inference efficiently is as critical as training a good model. As models grow larger and more complex, the challenge shifts from accuracy to throughput, latency, and cost optimization. This post introduces practical strategies, architectures, and tools used in 2025 to scale ML inference across CPUs, GPUs, and distributed environments.
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Tools: PyTorch, TensorFlow
An in-depth comparison of PyTorch and TensorFlow in 2025. This post explores their architectures, deployment strategies, performance features, and integration with modern MLOps tools to help engineers choose the right deep learning framework for their next AI project.
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Expert: distributed tuning with Ray Tune
Ray Tune is the premier framework for distributed hyperparameter optimization in 2025. This expert-level guide explores advanced scaling techniques, real-world integrations, and optimization strategies for orchestrating large-scale tuning across clusters, GPUs, and cloud environments.
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Empirical: batch vs streaming stores
This empirical post explores the modern trade-offs between batch and streaming data stores. Using benchmarks from real-world systems like Spark, Flink, and Pinot, it examines performance, cost, and operational complexity in 2025. Learn how unified architectures and hybrid designs are shaping the next generation of data processing systems.
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Best practices for evaluating clusters
Evaluating clustering models goes far beyond picking the highest silhouette score. This post explores modern best practices for evaluating clusters in unsupervised learning, combining internal and external validation metrics, visualization techniques, and domain-driven evaluation frameworks that leading data teams use in 2025 to ensure meaningful, actionable segmentation results.
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Tools: Evidently AI, WhyLabs
Evidently AI and WhyLabs are two leading tools shaping how teams monitor data drift and model health in production ML systems. This post explores their architectures, features, integrations, and best practices for using them together in modern data observability workflows.
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Empirical: algorithm benchmarks
Algorithm benchmarking defines the empirical backbone of modern computing. This article explores how high-performance teams measure, compare, and optimize algorithmic efficiency across CPUs, GPUs, and distributed systems â covering reproducibility, statistical rigor, and the tools that make empirical benchmarking a science rather than an art.
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Best practices for ensemble tuning
This post dives into modern best practices for ensemble tuning in machine learning. It covers effective hyperparameter optimization, meta-learning strategies, and workflow automation using frameworks like Optuna, Ray Tune, and AutoGluon. By following these methods, data scientists can maximize the predictive power and reliability of their ensembles in production.