Machine Learning

Quantization Techniques for LLM Inference: INT8, INT4, GPTQ, and AWQ

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Large language models have achieved remarkable capabilities, but their computational demands create a fundamental tension between performance and accessibility. A 70-billion parameter model in standard FP16 precision requires approximately 140GB of memory—far exceeding what’s available on consumer GPUs and even challenging high-end datacenter hardware. Quantization techniques address this challenge by reducing the numerical precision of … Read more

Nearest Neighbors Algorithms and KD-Tree vs Ball-Tree Indexing

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Nearest neighbors search stands as one of the most fundamental operations in machine learning and data science, underpinning everything from recommendation systems to anomaly detection, from image retrieval to dimensionality reduction techniques like t-SNE. Yet the seemingly simple task of finding the k closest points to a query point becomes computationally challenging as datasets grow … Read more

Building Scalable RLHF Pipelines for Enterprise Applications

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Reinforcement Learning from Human Feedback (RLHF) has emerged as the critical technique behind the most capable language models in production today. While the conceptual framework appears straightforward—collect human preferences, train a reward model, optimize the policy—building RLHF pipelines that scale to enterprise demands requires navigating a complex landscape of infrastructure challenges, data quality concerns, and … Read more

Probabilistic vs. Deterministic Machine Learning Algorithms: Understanding the Fundamental Divide

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In the landscape of machine learning, one of the most fundamental yet often misunderstood distinctions lies between probabilistic and deterministic algorithms. This divide isn’t merely a technical curiosity—it shapes how models make predictions, quantify uncertainty, handle ambiguous data, and ultimately serve real-world applications. Understanding when to employ each approach can be the difference between a … Read more

Cosine Similarity vs Dot Product vs Euclidean Distance

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Vector similarity metrics form the backbone of modern machine learning systems, from recommendation engines that suggest your next favorite movie to search engines that retrieve relevant documents from billions of candidates. Yet the choice between cosine similarity, dot product, and Euclidean distance profoundly affects results in ways that aren’t immediately obvious. A recommendation system using … Read more

Predicting Customer Dietary Preference Shifts with Structured Models

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The food industry faces an unprecedented challenge: customer dietary preferences no longer remain static throughout a lifetime or even a year. A customer who regularly ordered meat-heavy meals might suddenly shift to plant-based options. Another who avoided gluten for years might reintroduce it gradually. These transitions aren’t random—they follow patterns influenced by health diagnoses, life … Read more

How Multicollinearity Affects Linear Model Reliability

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Linear regression stands as one of the foundational tools in statistical modeling and machine learning, valued for its interpretability and mathematical elegance. Yet a subtle problem can undermine everything that makes linear models valuable: multicollinearity. When predictor variables exhibit strong correlations with each other, the reliability of coefficient estimates, statistical inference, and model interpretation deteriorates … Read more

When Logistic Regression Outperforms Deep Learning

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The narrative around machine learning often centers on deep learning’s remarkable achievements—neural networks mastering computer vision, natural language processing, and game playing with superhuman performance. This success story has created an implicit assumption that deep learning is always superior, that throwing more layers and parameters at a problem will inevitably yield better results. Yet in … Read more

Regularization Paths for Lasso vs Ridge vs Elastic Net

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Understanding how regularized regression models behave as you adjust their penalty parameters is fundamental to both model selection and gaining intuition about how regularization actually works. While most practitioners know that Lasso performs feature selection and Ridge shrinks coefficients smoothly, the real insight comes from examining regularization paths—visualizations showing how each coefficient evolves as the … Read more

Building Explainability Pipelines for SHAP Values at Scale

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Machine learning models have become increasingly complex, trading interpretability for accuracy as deep neural networks and ensemble methods dominate production deployments. Yet regulatory requirements, stakeholder trust, and debugging needs demand that we explain model predictions—not just what the model predicted, but why. SHAP (SHapley Additive exPlanations) values have emerged as the gold standard for model … Read more