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The Monty Hall Problem is a well-known brain teaser from which we can learn important lessons in Decision Making that are useful in general and in particular for data scientists. If you are not familiar with this problem, prepare to be perplexed . If you are, I hope to shine light on aspects that you […]

Quantization is one of the key techniques for reducing the memory footprint of large language models (LLMs). It works by converting the data type of model parameters from higher-precision formats such as 32-bit floating point (FP32) or 16-bit floating point (FP16/BF16) to lower-precision integer formats, typically INT8 or INT4. For example, quantizing a model to

The saying goes that 80% of data collected, stored and maintained by governments can be associated with geographical locations. Although never empirically proven, it illustrates the importance of location within data. Ever growing data volumes put constraints on systems that handle geospatial data. Common Big Data compute engines, originally designed to scale for textual data,

Bagging and boosting are two powerful ensemble techniques in machine learning – they are must-knows for data scientists! After reading this article, you are going to have a solid understanding of how bagging and boosting work and when to use them. We’ll cover the following topics, relying heavily on examples to give hands-on illustration of

In the world of entity resolution (ER), one of the central challenges is managing and maintaining the complex relationships between records. At its core, Tilores models entities as graphs: each node represents a record, and edges represent rule-based matches between those records. This approach gives us flexibility, traceability, and a high level of accuracy, but

With the amount of Data growing exponentially in the last few years, one of the biggest challenges has become finding the most optimal way to store various data flavors. Unlike in the (not so far) past, when relational databases were considered the only way to go, organizations now want to perform analysis over raw data

Survival Analysis is a statistical approach used to answer the question: “How long will something last?” That “something” could range from a patient’s lifespan to the durability of a machine component or the duration of a user’s subscription. One of the most widely used tools in this area is the Kaplan-Meier estimator. Born in the

In this article, we’ll talk about what Density Estimation is and the role it plays in statistical analysis. We’ll analyze two popular density estimation methods, histograms and kernel density estimators, and analyze their theoretical properties as well as how they perform in practice. Finally, we’ll look at how density estimation may be used as a

Summary of This Study Hardware choices – specifically hardware type and its quantity – along with training time, have a significant positive impact on energy, water, and carbon footprints during AI model training, whereas architecture-related factors do not. The interaction between hardware quantity and training time slows the growth of energy, water, and carbon consumption