Demand prediction when having N customers and M products

I have a csv file (dataset) with the following information:

    date    customer_id    product_id    quantity
11/02/2019        11            2212         2
11/02/2019        12            1116         10
07/04/2020        24            0088         4
22/04/2020        06            2212         7

This dataset represets the sales of a shop during the last 5 years (on daily basis).
The dataset contains N customers (~100) and M products (~2.5k)

I want to build a machine learning model that can predict the sold quantity of each product, for each customer.

For instance, today, I have 10 customers, each bought aroung 20 products with different quantitys.

How can I form this problem? probably it’s a regression task, but how can I build a model that can produce the needed outputs (predicting for each customer the product and its quantity)?

I saw some examples online, but all of them has a model for each item, (e.g. Demand Forecast using Machine Learning with Python), and in their cases they have few items.

In my case, since I have M products, should I build M models (prob. not)?