Connect BigML and Smaily to Build Intelligent Automations
Enable Integrations or automations with these events of BigML and Smaily
Enable Integrations or automations with these events of BigML and Smaily
Triggers
New Resource
Triggers when a new resource is created
Actions
Create Anomaly Score
Calculates the anomaly score of a data instance.
Create Centroid
Find out the closest cluster to your data instance
Create Prediction
Predict using a model , logistic regression or deepnets
Find Resource
Finds a resource
List Sources
Get all the sources.
List Datasets
Get all the dataset.
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Frequently Asked Questions
To start, connect both your BigML and Smaily accounts to viaSocket. Once connected, you can set up a workflow where an event in BigML triggers actions in Smaily (or vice versa).
Absolutely. You can customize how BigML data is recorded in Smaily. This includes choosing which data fields go into which fields of Smaily, setting up custom formats, and filtering out unwanted information.
The data sync between BigML and Smaily typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between BigML and Smaily. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
About BigML
BigML is a leading machine learning platform that provides a wide range of tools and services for creating, deploying, and managing machine learning models. It offers an intuitive interface and powerful APIs to help businesses and developers harness the power of machine learning for predictive analytics, anomaly detection, clustering, and more.
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About Smaily
Smaily is an intentionally simple tool made to create and send out beautiful email newsletters. We’ll help you increase the quality and results of your email marketing, while reducing preparation time and resources.
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