Thanks to the feedback provided by early adopters, our BigML app for Zapier has been acquiring new useful features, including improved support for additional ML algorithms and dynamic resource selection.
Support for new ML algorithms
The first version of our BigML Predict app only included support for model, ensembles, and logistic regressions. Now, you can also use clusters and anomaly detectors for your predictions and execute a WhizzML script, which can be great to automate more advanced use cases.
When you try to add an action from the BigML Predict app, you will now see a longer list of choices as shown below:
- Create Prediction (legacy): if you haven’t used the BigML Predict app before, you can safely ignore the “Create Prediction (legacy)” action.
- Execute WhizzML script: this action allows you to run a WhizzML script with a given set of arguments. Due to the way Zapier requires users to specify input fields, you will only be able to run WhizzML scripts that take “scalar” arguments.
- Create Anomaly Score: computes the anomaly score associated with a data instance by using an anomaly detector.
- Create Centroid: identifies the cluster that is closer to your input data instance.
- Create Ensemble Prediction: uses an ensemble to make a prediction.
- Create Prediction: uses a model or logistic regression to make a prediction.
When you include one of the actions listed above into your workflow, you will be given the chance to specify a few input fields:
- the Resource ID: a simple value of the form
ensemble/123456. The resource must exist in you BigML account, otherwise, the workflow execution will fail. You can either hard-code this value or use the “Find a
resource” option to select the proper resource dynamically based on a number of criteria. This will be further detailed below.
- Input Data: a list of values to be used as a prediction input. For each of them, you specify both the feature name and its actual value.
- Additional input arguments: to specify how the prediction should be calculated. Allowed arguments will vary with the prediction algorithm you choose. For example, an ensemble prediction allows you to specify how to handle missing values, as well as what kind of combiner to use, etc.
Dynamically selecting ML resources
If you joined our beta program, chances are you’ve noticed that the biggest new feature in our BigML Predict app is the “Find a resource” search option. It simply lets you specify a number of search criteria to identify a resource to use for predictions.
This means you can, for example, specify a project name and a resource type to identify the most recent resource of that type belonging to the specified project. The result of this operation is a Resource ID that you can use in any subsequent step of your Zapier workflow to further manipulate that resource. The image below displays all the search criteria you can use.
At a bare minimum, to search for a resource, you should provide its type, e.g., anomaly detector, ensemble, etc. If you only specify a resource type, the “Find a resource” search will select your latest resource of that type among all of your resources. Alternatively, you can make your search more specific by also providing any of the following information:
- Resource Name: the name of the resource you would like to select or a part of it.
- Resource Name: a tag associated with the required resource.
- Project Name: the name of the project your resource should belong to. You can also specify only a part of the project name.
- Resource Name: a tag associated with the project your resource should belong to.
- Mode: either Production or Development mode. If you don’t specify anything, the “Find a resource” search will look into your production resources by default.
If your search criteria aren’t specific enough to identify just one resource, the most recent one will be used.
To effectively include a search step in one of your Zapier workflows, you should link the
Resource ID field of your, e.g., Create Prediction action to the output provided by the search step, as shown in the picture below.
Get access to the improved BigML Predict app
We hope that the new features we added to BigML Predict for Zapier will help you better use Zapier to solve your ML automation problems.
If you are interested in giving the new BigML Predict app a try, please get in touch with us at email@example.com.