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:
- 1.Create the model on the dashboard. We automatically train and deploy it.
- 1.The model can be retrained manually on the dashboard later on when more data is collected.
- 2.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.
- 2.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 |
model_name | string | The name of the model. |
model_type | string | The model type. |
description | string | The model description. |
features | array | An array of strings with features. |
target | string | What is used to query with. |
output_type | string | The result of the query. |
versions | array | List of objects with model versions. |
Attributes | Type | Description |
training_status | string | A string with the training status. Possible values: STARTING, CREATED, TRAINING, PENDING, READY, RUNNABLE, FAILED, SUCCEEDED, INSUFFICIENT DATA, JOB CANCELLED. Default is CREATED. |
job_id | string | The AWS job ID. |
status_reason | string | The reason for the failure. Examples: deleting ds version, deleting ds model. |
human_id | string | A human readable ID. |
Last modified 2yr ago