Can AWS add a.i. to my Database that AWS hosts?

I believe Amazon SageMaker can be a good option for you. In short, it's a managed service that provides every developer or data scientist with the ability to build, train, and deploy machine learning (ML) models quickly.

Here's a general outline of how you might approach setting up SageMaker for data analytics:

1. Data Preparation: First, you'll need to prepare your data. This typically involves cleaning the data and possibly transforming it to a format that's suitable for machine learning. This process can be done using Amazon SageMaker Data Wrangler.

2. Model Training: Once your data is prepared, you can use SageMaker to train a machine learning model. SageMaker provides a number of built-in algorithms that you can use, or you can use your own custom algorithms, or some open-source public ones. In your case, you might use supervised learning algorithms such as decision trees or random forest, or you may want to consider deep learning models if you have a large amount of data.

3. Model Deployment: After training your model, you can use SageMaker to deploy it. This involves creating a model in SageMaker, creating an endpoint configuration, and then creating an endpoint that your application can use to make predictions.

4. Integration with Database: It isn't clear what database service you are using. But you can see more information here regarding data integration with SageMaker.

Remember, setting up a machine learning environment can be a complex process, and these steps are just a high-level overview. You'll likely need to dive deeper into each of these topics to fully understand how to set up your environment.