AzureAI
The foundational models for time series by Nixtla can be deployed on your Azure subscription. This page explains how to easily get started with TimeGEN-1 deployed as an Azure AI endpoint. If you use the
nixtla
library, it should be a drop-in replacement where you only need to change the client parameters (endpoint URL, API key, model name).
Deploying TimeGEN-1
Using the model
Once your model is deployed and provided that you have the relevant permissions, consuming it will basically be the same process as for a Nixtla endpoint.
To run the examples below, you will need to define the following environment variables:
AZURE_AI_NIXTLA_BASE_URL
is your api URL, should be of the formhttps://your-endpoint.inference.ai.azure.com/
.AZURE_AI_NIXTLA_API_KEY
is your authentication key.
How to use
Just import the library, set your credentials, and start forecasting in two lines of code!
pip install nixtla
import os
from nixtla import NixtlaClient
base_url = os.environ["AZURE_AI_NIXTLA_BASE_URL"]
api_key = os.environ["AZURE_AI_NIXTLA_API_KEY"]
model = "azureai"
nixtla_client = NixtlaClient(api_key=api_key, base_url=base_url)
nixtla_client.forecast(
...,
model=model,
)
Updated 9 days ago