Our APIs have Machine Learning (ML) capabilities baked into them. If you’re using our Building Blocks, you’re collecting data in a way that is already plugged into our ML tool set. These docs will explain what you can do with our ML tools and how to do it.
We have two main types of ML models, recommendations and similarities. We’re continuously working on expanding that list. All ML models described in these docs are different, but share a set of common features and parameters.
Once you’re plugged into our APIs, you have access to several out-of-the-box recommenders and similarity models that apply to the most common use cases in our Simple ML section
For more flexibility and more detailed description of , check out the Advanced ML section.
In general, you will go through the following three phases for all ML models:
Create the model on the dashboard. We automatically train and deploy it.
The model can be retrained manually on the dashboard later on when more data is collected.
An automatic evaluation that shows the validity of the model is created while the model is trained. This indicates how predictive the results should be.
Query the recommendations or predictions online.
The name of the model.
The model type.
The model description.
An array of strings with features.
What is used to query with.
The result of the query.
List of objects with model versions.
A string with the training status. Possible values:
It the AWS job ID
Will get the reason of the failure if there is.
Examples: deleting ds version, deleting ds model.
It is a human readable ID.