Over a million developers have joined DZone.
{{announcement.body}}
{{announcement.title}}

How to Automate Your Predictions With BigML and Zapier

DZone's Guide to

How to Automate Your Predictions With BigML and Zapier

Want to automate your predictive analytics capabilities? Read on to get started on automation using BigML and a web-based automation tool, Zapier.

· AI Zone
Free Resource

Insight for I&O leaders on deploying AIOps platforms to enhance performance monitoring today. Read the Guide.

At BigML, we are always striving to provide our users with the best options to integrate machine learning into their workflows — be it through our bindings available for the major programming languages, our BigMLer command-line tool, BigML for Google Sheet Add-on, or our proprietary Domain Specific Language WhizzML. Our latest effort in this direction is providing the means to integrate the BigML platform into web-based automation tools such as Zapier, a web service that allows end users to create workflows across different web applications, including Google Drive, Salesforce, Gmail, and many others.

Today, we’d like to give you a preview of the new BigML Predict Action for Zapier, which will allow users to make predictions using models, ensembles, logistic regressions, or clusters in their BigML account as part of a larger Zapier workflow. Imagine the following scenarios...

An IoT device monitors the insulin and glucose levels and the blood pressure of a patient and periodically, for example, every hour, and sends its data to some remote storage service like a Google Sheet or an email server. Whenever new data comes in, a Zapier trigger reads it in and passes it to the BigML Predict action, which will predict the likeliness of diabetes. The predicted outcome is then used to trigger the sending of an email in case the confidence of a diabetes diagnosis is higher than a given threshold.

An e-commerce service stores all processed orders in Salesforce, along with the data about the buyer, the payment, and any other significant data to describe the transaction, such as whether the delivery was disputed, the product was returned, a refund required, etc. For each new order coming in, you could trigger a prediction using a BigML model to evaluate the likeliness of that transaction to fail for whatever reason. If the prediction confidence is higher than a given threshold, you can use another Zapier action to flag the transaction in Salesforce as requiring ad-hoc tracking by a human controller.

These scenarios represent just a couple of possibilities on how to integrate the BigML Predict action in a Zapier workflow. The following images show a simple Zapier workflow that polls a Google Sheet for new input data, uses those inputs to make a prediction by using a given model, then stores the prediction in a separate sheet.

zapier-1

Using Zapier UI, you can easily map the data in your Google Sheet as input to a BigML Predict action:

z1

Once you enable your Zapier workflow, it will check the input Google Sheet for new data and automatically store the prediction in a second Google Sheet:

z3

The BigML Predict action for Zapier is available as a beta for any BigML customer that is willing to try it out. 

TrueSight is an AIOps platform, powered by machine learning and analytics, that elevates IT operations to address multi-cloud complexity and the speed of digital transformation.

Topics:
ai ,automation ,machine learning ,predictive analytics ,tutorial ,bigml ,zapier

Published at DZone with permission of Sergio De Simone, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}