What do you want to automate with BigML?
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Overview#
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}`,
},
})
},
})
Example Use Cases#
Automated Data Analysis Pipeline: Set up an event-driven workflow where a new data file uploaded to Google Drive triggers a Pipedream workflow. The workflow fetches the file, processes the data, and sends it to BigML to update a dataset or train a new model. Gain insights or predictions immediately without manual intervention.
Real-time Prediction Service: Create a real-time prediction service by setting up an HTTP endpoint in Pipedream. When data is posted to this endpoint, it's forwarded to BigML to generate a prediction using a pre-trained model. The prediction is then sent back as the HTTP response, allowing for real-time integration into applications or services.
IoT Data Monitoring and Response: Integrate IoT device data streams with BigML via Pipedream. Monitor sensor data in real-time, pass it to BigML for analysis, and use the results to trigger notifications or actions in other services like Slack or email. This can be used for predictive maintenance, anomaly detection, or environmental monitoring.
Popular BigML Integrations#
Popular BigML 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.
Authentication#
Name Slug: bigmlBigML uses API keys for authentication. When you connect your BigML account, Pipedream securely stores the keys so you can easily authenticate to BigML APIs in both code and no-code steps.
Sign in and copy your Username visible from the BigML Dashboard.
To retrieve your API key,
- Navigate to your BigML account and sign in
- Click on the Account menu on the top right
- Go to “API key”
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