BigML

Machine Learning made beautifully simple. A company-wide platform that runs in any cloud or on-premises to operationalize Machine Learning in your organization.

Integrate the BigML API with the Amazon SES API

Setup the BigML API trigger to run a workflow which integrates with the Amazon SES API. Pipedream's integration platform allows you to integrate BigML and Amazon SES remarkably fast. Free for developers.

Create Email Template with Amazon SES API on New Model Created from BigML API
BigML + Amazon SES
 
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Create Email Template with Amazon SES API on New Prediction Made from BigML API
BigML + Amazon SES
 
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Get Email Template with Amazon SES API on New Model Created from BigML API
BigML + Amazon SES
 
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Get Email Template with Amazon SES API on New Prediction Made from BigML API
BigML + Amazon SES
 
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Send Email with Amazon SES API on New Model Created from BigML API
BigML + Amazon SES
 
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New Model Created from the BigML API

Emit new event for every created model. See docs here.

 
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New Prediction Made from the BigML API

Emit new event for every made prediction. See docs here.

 
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Create Batch Prediction with the BigML API

Create a batch prediction given a Supervised Model ID and a Dataset ID. See the docs.

 
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Create Email Template with the Amazon SES API

Create a HTML or a plain text email template. See the docs

 
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Create Model with the BigML API

Create a model based on a given source ID, dataset ID, or model ID. See the docs.

 
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Get Email Template with the Amazon SES API

Get an email template. See the docs

 
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Create Source (Remote URL) with the BigML API

Create a source with a provided remote URL that points to the data file that you want BigML to download for you. See the docs.

 
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Overview of BigML

The BigML API offers a suite of machine learning tools that enable the creation and management of datasets, models, predictions, and more. It's a powerful resource for developers looking to incorporate machine learning into their applications. Within Pipedream, you can leverage the BigML API to automate workflows, process data, and apply predictive analytics. By connecting BigML to other apps in Pipedream, you can orchestrate sophisticated data pipelines that react to events, perform analyses, and take action based on machine learning insights.

Connect BigML

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    bigml: {
      type: "app",
      app: "bigml",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://bigml.io/andromeda/source`,
      params: {
        username: `${this.bigml.$auth.username}`,
        api_key: `${this.bigml.$auth.api_key}`,
      },
    })
  },
})

Overview of Amazon SES

Amazon Simple Email Service (SES) is a powerful cloud-based email sending service designed to help digital marketers and application developers send marketing, notification, and transactional emails. With the SES API, you can reliably send emails at scale, manage sender reputations, view email sending statistics, and maintain a high deliverability rate. Leveraging Pipedream's capabilities, you can integrate SES seamlessly into serverless workflows, automate email responses based on triggers from other apps, and analyze the effectiveness of your email campaigns by connecting to data analytics platforms.

Connect Amazon SES

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module.exports = defineComponent({
  props: {
    amazon_ses: {
      type: "app",
      app: "amazon_ses",
    }
  },
  async run({steps, $}) {
    const AWS = require("aws-sdk")
    const { accessKeyId, secretAccessKey } = this.amazon_ses.$auth
    
    const ses = new AWS.SES({
      accessKeyId, 
      secretAccessKey,
      region: 'us-east-1',
    })
    
    const sesParams = {
      Destination: {
        ToAddresses: ["<your email here>"],
      }, 
      Message: {
        Body: {
          Html: {
            Charset: "UTF-8", 
            Data: "<h1>This is a test</h1>",
          }, 
            Text: {
            Charset: "UTF-8", 
            Data: "This is a test",
          }
        }, 
        Subject: {
          Charset: "UTF-8", 
          Data: "Test email",
        }
      },
      Source: "<your from address here", 
    };
    
    this.resp = await ses.sendEmail(sesParams).promise()
  },
})