Building an ML Portfolio That Gets Noticed: Beyond GitHub Repositories

Abstract

This article examines the evolution of machine learning portfolios beyond traditional code repositories, highlighting strategies for effectively showcasing problem-solving competencies that resonate with industry employers. The article looks at methods for recording decision intelligence throughout the development lifecycle, strategies for balancing portfolio breadth and depth across career stages, frameworks for choosing strategic projects that show business value, and multifaceted portfolio presentation techniques. By integrating visualization, storytelling, and communication elements alongside technical implementations, practitioners can create portfolios that demonstrate not just coding ability but the full spectrum of competencies required for successful ML implementation in organizational contexts. The article provides practical guidance for ML practitioners seeking to create portfolios that effectively communicate their capacity to deliver business value through thoughtful problem formulation, rigorous methodology, and effective communication—the true differentiators in today's competitive ML job market.