Integrate the AWS API with the BigML API

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

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Popular Ways to Connect AWS with BigML#

Create Batch Prediction with BigML API on New Scheduled Tasks from AWS API

AWS + BigML

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Create Batch Prediction with BigML API on New SNS Messages from AWS API

AWS + BigML

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Create Model with BigML API on New Scheduled Tasks from AWS API

AWS + BigML

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Create Model with BigML API on New SNS Messages from AWS API

AWS + BigML

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Create Source (Remote URL) with BigML API on New Scheduled Tasks from AWS API

AWS + BigML

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Popular AWS and BigML Triggers#

New Scheduled Tasks from the AWS API

Creates a Step Function State Machine to publish a message to an SNS topic at a specific timestamp. The SNS topic delivers the message to this Pipedream source, and the source emits it as a new event.

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New SNS Messages from the AWS API

Creates an SNS topic in your AWS account. Messages published to this topic are emitted from the Pipedream source.

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New Inbound SES Emails from the AWS API

The source subscribes to all emails delivered to a specific domain configured in AWS SES. When an email is sent to any address at the domain, this event source emits that email as a formatted event. These events can trigger a Pipedream workflow and can be consumed via SSE or REST API.

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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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Popular AWS and BigML Actions#

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 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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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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CloudWatch Logs - Put Log Event with the AWS API

Uploads a log event to the specified log stream. See docs

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DynamoDB - Create Table with the AWS API

Creates a new table to your account. See docs

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Overview of AWS#

The AWS API unlocks endless possibilities for automation with Pipedream. With this powerful combo, you can manage your AWS services and resources, automate deployment workflows, process data, and react to events across your AWS infrastructure. Pipedream offers a serverless platform for creating workflows triggered by various events that can execute AWS SDK functions, making it an efficient tool to integrate, automate, and orchestrate tasks across AWS services and other apps.

Connect AWS#

import AWS from 'aws-sdk'

export default defineComponent({
  props: {
    aws: {
      type: "app",
      app: "aws",
    }
  },
  async run({steps, $}) {
    const { accessKeyId, secretAccessKey } = this.aws.$auth
    
    /* Now, pass the accessKeyId and secretAccessKey to the constructor for your desired service. For example:
    
    const dynamodb = new AWS.DynamoDB({
      accessKeyId, 
      secretAccessKey,
      region: 'us-east-1',
    })
    
    */
  },
})

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#

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}`,
      },
    })
  },
})

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