Marvelous MLOps #19: What do ML engineers deploy: batch use case

In the article Deployment strategies for ML products, we talked about the need for 3 environments with access to production data (DEV, ACC, PRD) and how those environments are used in the ML deployment process. We have touched a bit on what exactly is being deployed, but it is good to come up with some concrete examples.

I will take a very common example from the retail industry, a use case with probably the most impact for any retailer: demand forecast for a warehouse or stores. Typically, we are talking about multiple models here: one for each product category, and there are tens, or hundreds of them.

Steps involved in the deployment

Demand forecast is usually implemented as a batch process, where predictions for coming x days are delivered daily: via SFTP transfer, or via writing to a database. What are the steps involved to make it happen?

• Data preprocessing. Usually, there is one big table where new data is processed and added incrementally each day. This table contains features needed for model retraining and model inference.
• (Conditional) model retraining. The model can be retrained periodically (for example, every week), or only when significant data drift occurs. Otherwise, the latest artifact is used.
• Model inference (generation of predictions).
• Delivery of predictions

Read further here: https://marvelousmlops.substack.com/p/what-do-ml-engineers-deploy-batch