Taylor Scott Amarel

Experienced developer and technologist with over a decade of expertise in diverse technical roles. Skilled in data engineering, analytics, automation, data integration, and machine learning to drive innovative solutions.

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How to Scale Data Analysis and Machine Learning Workflows Using Apache Spark: A Practical Guide

Introduction: The Need for Scalable Data Analysis and Machine Learning In today’s data-driven world, the ability to analyze massive datasets and build sophisticated machine learning models is paramount. However, traditional data analysis tools often struggle to cope with the sheer volume and velocity of modern data. This is where Apache Spark steps in, offering a

Building Scalable Data Pipelines for Machine Learning: A Practical Guide

Introduction: The Backbone of Scalable Machine Learning In the 2020s, machine learning (ML) has moved beyond experimentation and into production. But deploying ML models at scale presents a significant challenge: building robust and scalable data pipelines. These pipelines are the backbone of any successful ML application, responsible for ingesting, transforming, storing, and delivering data to