MLOps aims to unify ML system development

AI-driven organizations are using data and machine learning to solve their hardest problems and are reaping the rewards. "Companies that fully absorb AI in their value-producing workflows by 2025 will dominate the 2030 world economy with 120% cash flow growth,"1 according to McKinsey Global Institute. Machine learning (ML) systems have a special capacity for creating technical debt if not managed well. They have all of the maintenance problems of traditional code plus an additional set of ML-specific issues: ML systems have unique hardware and software dependencies, require testing and validation of data as well as code, and as the world changes around us deployed ML models degrade over time. Moreover, ML systems underperform without throwing errors, making identifying and resolving issues especially challenging.