What does MLOps primarily contribute to in an organization?

MLOps, which stands for Machine Learning Operations, primarily contributes to evolving practices within an organization by streamlining and standardizing the process of deploying and maintaining machine learning models in production. It brings together teams involved in data science, operations, and software engineering to facilitate better collaboration and communication. By implementing MLOps, organizations can establish workflows that enhance the way models are built, tested, deployed, and monitored. This leads to a more systematic approach to integrating machine learning into business processes, enabling organizations to adapt to changing requirements, improve model performance, and continuously innovate.

While MLOps can indirectly impact employee engagement, cost reduction, and market expansion through improved efficiencies and better outcomes, its core contribution lies in unfolding and refining best practices in machine learning operations, which can ultimately transform how organizations leverage artificial intelligence.