It takes more than models.
Building the whole pipeline used for model generation and utilization in production requires multiple capabilities. Data ingestion, cleansing, labeling, model training infrastructure, model performance monitoring, and feedback collection are must-haves. Furthermore, these elements need to be automated to account for frequently changing data.
Evaluate the data strategy.
Design and build the ingestion and model deployment pipeline.
Monitor the model's performance and close feedback loop.