To borrow a baseball analogy, organizations can hit a home run with a one-off machine learning model but still lose the game if they don’t bring machine learning to scale.
In this Horizon Report from Stratascale (an SHI company), we explore how enterprises should integrate data models into their complex operating environments.
Specifically, technology and business leaders should use machine learning operations (MLOps) to improve the efficiency of data science projects and maintain the relevance of deployed assets.
Companies that operationalize their machine learning activities achieve the most success. For example, Uber has over 1,000 models in production concurrently, enabled by the development of their own pipeline and workflow management platform, Michelangelo. Booking.com has over 150 models in production, while CapitalOne conducts over 80,000 big data experiments a year.
Enterprises don’t have to reach the level of these digital titans to benefit from MLOps but they should integrate an MLOps focus on culture, design, and infrastructure to enable the rapid development, deployment, and iteration of high-quality machine learning models while still allowing the freedom to explore and experiment.
To learn more, read the full report.
With a background in data analytics, financial planning and analysis, and economics, Joe Granick has focused on optimized decision making by leveraging statistics, mathematics, and machine learning.