To get started, I'd pick the most important business problem you have and then solve it using the simplest machine learning approach. You mentioned using Pytor

To get started, I'd pick the most important business problem you have and then solve it using the simplest machine learning approach. You mentioned using Pytorch. Instead, I recommend a classical machine learning using a library like scikit-learn (https://scikit-learn.org/ https://scikit-learn.org/). Use a random forest classifer and you'll get pretty good results out of the box. If your data is in a postgres database across multiple tables, you will likely have to perform feature engineering in order to get it machine learning ready. For that, I recommend a library for automated feature engineering called Featuretools (http://github.com/featuretools/featuretools/ http://github.com/featuretools/featuretools/). Here's a good article to get started with it (https://towardsdatascience.com/automated-feature-engineering-in-python-99baf11cc219 https://towardsdatascience.com/automated-feature-engineering...) Finally, you will need to define a prediction problem and extract labeled training examples. I see people in this thread have suggested ideas of problems to work on. The key here is make sure that you pick a problem that you can both predict and take an action based off the prediction. For example, you could predict that there will be an influx of shipments to fulfill tomorrow, but that might not be enough to time to hire more people to help you fulfill them. If you're curious what the process looks like end-to-end check out this blog series on a generalized framework for solving machine learning problems that was applied to customer churn prediction: https://blog.featurelabs.com/how-to-create-value-with-machine-learning/ https://blog.featurelabs.com/how-to-create-value-with-machin... Full disclosure: I work for Feature Labs and develop Featuretools.