Lucas and Luna tackle the often-overlooked chasm between a promising prototype and a production model that actually drives business value. They dive into a 2023 Gartner finding that 85 percent of data science projects never reach deployment, and unpack the three biggest culprits: misaligned incentives between data scientists and engineers, brittle code that can't scale, and missing infrastructure for monitoring and retraining. Lucas draws on his experience covering MLOps startups to explain why 'it works on my machine' is a design flaw, not a joke. Luna pushes back on the idea that better tools alone solve the problem, arguing that organizational culture and cross-functional communication matter just as much. The episode lands on a concrete framework: start with the deployment environment, build backward, and treat the model as a living system — not a handoff artifact.

#DataScience #MLOps #ModelDeployment #DataScienceFailures #Gartner #ProductionML #MachineLearning #Technology #BusinessPodcast #FexingoBusiness #LucasAndLuna #DataPipelines #ModelMonitoring #DataEngineering #CrossFunctionalTeams #DeploymentStrategy #TechTalk #Podcast

Keep every episode free: buymeacoffee.com/fexingo