Vol. 1, Issue 1, Part A (2024)

As the volume of data generated across industries continues to grow exponentially, scalable machine learning pipelines have become essential for extracting actionable insights from massive datasets. Traditional machine learning workflows struggle to handle large-scale data due to computational limitations, memory constraints, and inefficient data processing frameworks. This paper examines the architectural design, critical components, and implementation strategies for building scalable machine learning pipelines capable of processing large data sets efficiently. We discuss distributed data storage, parallel data preprocessing, model training across multiple nodes, and serving models in production environments. Additionally, the paper evaluates state-of-the-art frameworks such as Apache Spark MLlib, TensorFlow Extended (TFX), and Kubernetes-based pipeline orchestration. The challenges related to data partitioning, model reproducibility, fault tolerance, and real-time scalability are also addressed. Our analysis highlights best practices and emerging trends that will define the next generation of large-scale machine learning systems.