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 HTTP / Webhook API
Setup the BigML API trigger to run a workflow which integrates with the HTTP / Webhook API. Pipedream's integration platform allows you to integrate BigML and HTTP / Webhook remarkably fast. Free for developers.
Popular Ways to Connect BigML with HTTP / Webhook#
Popular BigML and HTTP / Webhook Triggers#
Get a URL and emit the full HTTP event on every request (including headers and query parameters). You can also configure the HTTP response code, body, and more.
Get a URL and emit the HTTP body as an event on every request
Emit new event when the content of the URL changes.
Popular BigML and HTTP / Webhook Actions#
Create a batch prediction given a Supervised Model ID and a Dataset ID. See the docs.
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.
Send an HTTP request using any method and URL. Optionally configure query string parameters, headers, and basic auth.
Send an HTTP GET request to any URL. Optionally configure query string parameters, headers and basic auth.
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}`,
},
})
},
})
Connect HTTP / Webhook#
// To use any npm package on Pipedream, just import it
import axios from "axios"
export default defineComponent({
async run({ steps, $ }) {
const { data } = await axios({
method: "GET",
url: "https://pokeapi.co/api/v2/pokemon/charizard",
})
return data.species
},
})
Community Posts#
