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Create a batch prediction given a Supervised Model ID and a Dataset ID. See the docs.
Write custom Node.js code and use any of the 400k+ npm packages available. Refer to the Pipedream Node docs to learn more.
Create a model based on a given source ID, dataset ID, or model ID. See the docs.
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.
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.
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}`,
},
})
},
})
Develop, run and deploy your Node.js code in Pipedream workflows, using it between no-code steps, with connected accounts, or integrate Data Stores and File Stores.
This includes installing NPM packages, within your code without having to manage a package.json
file or running npm install
.
Below is an example of installing the axios
package in a Pipedream Node.js code step. Pipedream imports the axios
package, performs the API request, and shares the response with subsequent workflow steps:
// To use previous step data, pass the `steps` object to the run() function
export default defineComponent({
async run({ steps, $ }) {
// Return data to use it in future steps
return steps.trigger.event
},
})