Organizations are becoming adept at launching projects that test their ability to use data, analytics, Machine Learning (ML), and Artificial Intelligence (AI). Data scientists tinker with data sets and analytical models, providing their organizations with the ability to understand trends, test decisions, identify new opportunities, sharpen marketing programs and shape recruitment strategies. These highly-trained data science teams can build sophisticatedv ystems. They can identify missing data values. And they know when their models are going awry. However, data scientists often fail when rolling out and propagating their systems for use by teams across the organization.

The failure can be attributed to several reasons. For example, a home insurance organization’s data science team may be using property prices that are not relevant anymore. In production, this model will fail because the data sets required are different. Further, the data used in the lab may be limited. The model may become difficult to scale or degrade with time in real-life applications. Or an organization may feel the process of organization-wide adoption involves multiple teams, which can become challenging to manage. Every large organization has experienced the pain of moving projects from data labs into practical enterprise environments. Before the transition to enterprise-wide usage, there are many challenges to overcome.

To successfully productionize and roll out stable enterprise-wide MLOps, an organization should establish standards. These standards could include the data infrastructure required for the ML lifecycle, data engineering methodologies, ML model engineering, testing, code library/ scalability, model governance, security, tools for MLOps teams, etc.