What do you want to automate
with BigML and Python?
Prompt, edit and deploy AI agents that connect to BigML, Python and 2,500+ other apps in seconds.
Trusted by 1,000,000+ developers from startups to Fortune 500 companies
Popular BigML and Python Actions#
Create a batch prediction given a Supervised Model ID and a Dataset ID. See the docs.
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python 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.
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}`,
},
})
},
})
Overview of Python#
Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow
This includes installing PyPI packages, within your code without having to manage a requirements.txt file or running pip.
Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:
Connect Python#
def handler(pd: "pipedream"):
# Reference data from previous steps
print(pd.steps["trigger"]["context"]["id"])
# Return data for use in future steps
return {"foo": {"test":True}}Trusted by 1,000,000+ developers from startups to Fortune 500 companies