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

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PostgreSQL

Integrate the BigML API with the PostgreSQL API

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

Popular Ways to Connect BigML with PostgreSQL

Create Batch Prediction with BigML API on New Column from PostgreSQL API
PostgreSQL + BigML
 
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Create Batch Prediction with BigML API on New or Updated Row from PostgreSQL API
PostgreSQL + BigML
 
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Create Batch Prediction with BigML API on New Row Custom Query from PostgreSQL API
PostgreSQL + BigML
 
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Create Batch Prediction with BigML API on New Row from PostgreSQL API
PostgreSQL + BigML
 
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Create Batch Prediction with BigML API on New Table from PostgreSQL API
PostgreSQL + BigML
 
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Popular BigML and PostgreSQL Triggers

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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New Column from the PostgreSQL API

Emit new event when a new column is added to a table. See the documentation

 
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New or Updated Row from the PostgreSQL API

Emit new event when a row is added or modified. See the documentation

 
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New Row from the PostgreSQL API

Emit new event when a new row is added to a table. See the documentation

 
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Popular BigML and PostgreSQL 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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Delete Row(s) with the PostgreSQL API

Deletes a row or rows from a table. See the documentation

 
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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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Execute Custom Query with the PostgreSQL API

Executes a custom query you provide. See the documentation

 
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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 PostgreSQL

On Pipedream, you can leverage the PostgreSQL app to create workflows that automate database operations, synchronize data across platforms, and react to database events in real-time. Think handling new row entries, updating records from webhooks, or even compiling reports on a set schedule. Pipedream's serverless platform provides a powerful way to connect PostgreSQL with a variety of apps, enabling you to create tailored automation that fits your specific needs.

Connect PostgreSQL

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import postgresql from "@pipedream/postgresql"

export default defineComponent({
  props: {
    postgresql,
  },
  async run({ steps, $ }) {
    // Component source code:
    // https://github.com/PipedreamHQ/pipedream/tree/master/components/postgresql

    const queryObj = {
      text: "SELECT NOW()",
      values: [], // Ignored since query does not contain placeholders
    };
    const { rows } = await this.postgresql.executeQuery(queryObj);
    return rows;
  },
})
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