Intro to ML
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 a few main types of ML models, recommendation, similarity and complementary. Just contact us if you'd like to expand that list. All ML models described in these docs share a set of common features and parameters.
Once you’re plugged into our APIs, you have access to several out-of-the-box recommendation and similarity models that apply to the most common use cases in our Simple ML section
You will go through the following 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.
In order for you to be able to train models, you have to have some data or transaction history, e.g. users, products, orders and/or payments on our platform.
If you have no or a small amount of data, you will see the INSUFFICENT_DATA training status.
Attributes | Type | Description |
|
| The name of the model. |
|
| The model type. |
|
| The model description. |
|
| An array of strings with features. |
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| What is used to query with. |
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| The result of the query. |
|
| List of objects with model versions. |
Attributes | Type | Description |
|
| A string with the training status. Possible values: |
|
| The AWS job ID. |
|
| The reason for the failure. Examples: deleting ds version, deleting ds model. |
|
| A human readable ID. |
