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Make predictions with BigML whenever Google Sheets rows are created
Do you want a quick way to make a prediction from data added to a Google Sheets spreadsheet? Create a suitable BigML model to handle your data and prediction objective, setup this integration, and whenever you add a new row to the spreadsheet, a new prediction will be created in your BigML account. This is the easiest way to integrate your source of data with BigML.
How this Google Sheets - BigML integration works
- Someone adds a new row to your Google Sheet
- Zapier creates a prediction on BigML using that data.
What you need
- Google Sheets
Post Slack channel notifications whenever new predictions are created in BigML
If you use Slack to communicate inside your team, this Zapier automation allows you to share any new predictions that are created in BigML with your coworkers. For example, this could be the latest prediction about the outlook of a given stock option.
Note: This does not necessarily need to be a prediction. You could share whatever kind of resource you want, for example, the latest version of a given model, so all of your coworkers know.
How this BigML-Slack integration works
- A new prediction is created in your BigML account
- Zapier sends the prediction outcome to a Slack channel
What you need
Create BigML predictions from Google Forms responses
When someone fills out your Google Forms form or survey, create a BigML prediction using that data. For example, if you send your customers a survey about their food preferences, you can use a BigML model to predict the likeliness they might buy a given product. The BigML model would be created by collecting your data about previous customers' food preferences and whether they bought that product.
Note: This Zapier integration doesn't include a ready-made BigML model to use for your predictions. You can build the model using the BigML Dashboard using your available data.
How this Google Forms-BigML integration works
- Your customer completes the Google Forms survey
- Zapier creates a BigML prediction with the customer responses using the model of your choosing
- Google Forms
Classify incoming Gmail messages using BigML topic modeling
It's often helpful to classify emails based on their content. For example, you could have a generic firstname.lastname@example.org address that receives emails intended for any department in the company (e.g., sales, finance, marketing, tech support, etc.) If you let BigML learn from your email archive which traits to use for distinguishing emails directed to the different departments, you can use it in this Zapier integration to process any new incoming Gmail email message and forward it to the relevant department.
Note: This Zapier integration doesn't include any ready-to-use BigML model to classify your emails. You can train one such model with the BigML dashboard using your email archive.
How this Gmail-BigML integration works
- A new email is received on your Gmail account
- Zapier classifies your email based on its content using your BigML model
- Zapier uses the predicted email address to forward the email to the most relevant department
Create recommendations using BigML for each order received on Amazon Seller Central
For each new order received through Amazon Seller Central, create a recommendation to suggest your customer other products they could be interested in. Use this Zapier automation to leverage all sales information you have to predict which products your customers can be interested in buying.
Note: This Zapier integration doesn't include a recommending system tailored to your business. You can create one using your past sales information with the BigML Dashboard.
How this Amazon Seller Central-BigML integration works
- A customer places an order on Amazon Seller Central
- Zapier creates a recommendation using BigML based on the order data
- Amazon Seller Central
Create Trello cards for new BigML resources
Trello allows your team to get in sync on heterogeneous tasks to reach a common goal. If you use BigML in your organization, it is possible that different people may need to work together on different tasks, e.g., someone in charge of data collection creates a new file somewhere; later a data engineer uses it to train a model; an executive prepares a presentation to explain the benefits of the new model; finally, marketing or sales people use that model for predicting interesting stuff and drive their further actions.
This integration allows you to create Trello cards for relevant events in these kind of workflows, e.g., a data wrangler uploading the latest data to BigML or a data engineer creating the latest ML model version from it, so your team can collaborate more efficiently. This is both helpful to get a team in sync on the various steps to create an ML workflow, as well as to combine it with other aspects of a Trello project that are not necessarily ML related.
How this BigML-Trello integration works
- A new resource is created in BigML
- Zapier creates a new Trello card so everyone knows that task has been carried through and all involved parties can move to the next step
Use BigML to find out the topics in a favorited Tweets and store them in Google Sheets
If you like to favorite tweets, then this integration will allow you to automatically detect the topics those tweets are about and keep track of them in a Google Sheets spreadsheet. This will allow you to build an archive of relevant tweets with an associated list of topics. For example, if you train a BigML model to detect which product of yours is mentioned in a tweet, or which product category, by favoriting all those tweets you can build a list of relevant tweets automatically classified by product or product category. As an additional example, you might want to classify your favorite tweets based on their topic being about music, movies, TV-series, etc.
Note: This Zapier integration doesn't include a ready-to-use Topic Modelling model. You are expected to train one from your own data.
How this Twitter-BigML-Google Sheets integration works
- Zapier watches your Twitter account for favorited Tweets
- Whenever you favorite a new Tweet, Zapier will use BigML to detect the topic distribution of that Tweet
- Zapier will store the list of topics the Tweet is about in Google Sheets
- Google Sheets
Assign BigML anomaly scores to new Google Forms responses and store them in Google Sheets
If you use Google Forms to survey your customers, you could want to spot those responses that look "anomalous" to you, according to your own definition of what an anomalous response is. Even better, if you label past responses you received as "normal" or "anomalous", you can train a BigML model that is able to automatically spot "anomalous" responses for you. This integration allows you to automatically assign an anomaly score to all new responses you get and update your forms data with that anomaly score.
Note: This Zapier integration does not provide a ready-to-use anomaly detector. You can train one using the BigML Dashboard with your own data.
How this Google Forms-BigML-Google Sheets integration works
- A customer sends in their responses to a Google Form
- Zapier uses BigML to associate an anomaly score to it
- The anomaly score is stored in a Google Sheets spreadsheet
- Google Forms
- Google Sheet
Tweet from your RSS feed and automatically add hashtags using BigML
Do you want to tweet all new posts on your RSS feed without the hassle of adding hastags to the Tweets manually? You can use this integration to use a BigML model to identify the most appropriate tags for each new post, then Tweet it.
Note: This Zapier integration does not include a ready-to-use BigML model that is able to assign hashtags as per your liking. You can train one using a sample of your past posts with the BigML Dashboard.
How this RSS by Zapier-BigML-Twitter integration works
- A new post is added to your RSS source
- Zapier uses BigML to classify it and find out what hashtags are more relevant for it
- Zapier tweets it and adds the relevant hastags
- RSS by Zapier
Use BigML to do sentiment analysis of Tweets that mention you and post the results to a Slack channel
Maybe you get a lot of mentions and want to make sure you do not ever miss a reply to a Tweet showing gratitude, or anger, or whatever fits your case. Once activated, this integration can help you to detect all Tweets showing a given sentiment, so you can better handle them.
Note: This integration does not provide a ready-made sentiment analysis ML model. You are required to build one using your Tweet history.
How this Twitter-BigML-Slack integration works
- Zapier monitors Tweets that mention you
- Whenever a new Tweet mentions you, Zapier uses BigML to detect its prevalent sentiments
- The Tweet and its detected sentiments are posted to a Slack channel
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Launched on Zapier April 11, 2018
Zapier combines Triggers (like "New Resource") and Actions (like "Create Prediction") to complete an action in one app when a trigger occurs in another app. These combos—called "Zaps"—complete your tasks automatically.
The following BigML Triggers, Searches, and Actions are supported by Zapier:
Predict using a model, logistic regression, or deepnets.
Find out the closest cluster to your data instance.
Calculates the anomaly score of a data instance.
Calculate all topic probabilities for a given document.
Triggers when a new resource is created.
Finds a resource.