AI/ML operationalization refers to the process of deploying machine learning models and artificial intelligence systems into production environments, so that they can be used to solve real-world problems and support business processes. This involves several steps, including:

Building and training a machine learning model: This involves using data and algorithms to create a model that can make predictions or take actions based on input data.

Testing and validation: Before deploying the model into production, it is important to ensure that it is accurate and reliable by testing it on a representative sample of data.

Deployment: Once the model has been tested and validated, it can be deployed into a production environment, where it can be used to support business processes and make decisions.

Monitoring and maintenance: After deployment, it is important to monitor the model's performance and make any necessary updates or adjustments to ensure that it continues to function effectively.

AI/ML operationalization requires careful planning and coordination to ensure that the models and systems are reliable, scalable, and able to support the needs of the business.