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We can fine-tune TimeGPT by specifying the finetune_steps parameter. 
import pandas as pd
from nixtla import NixtlaClient

nixtla_client = NixtlaClient(
    # defaults to os.environ.get("NIXTLA_API_KEY")
    api_key = 'my_api_key_provided_by_nixtla'
)
👍 Use an Azure AI endpoint To use an Azure AI endpoint, remember to set also the base_url argument: nixtla_client = NixtlaClient(base_url="you azure ai endpoint", api_key="your api_key") 
# Read data
df = pd.read_csv("https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv")

# Forecast with fine-tuning.
# Here, we fine-tune for 5 steps
forecast_df = nixtla_client.forecast(
    df=df,
    h=12,
    finetune_steps=5,
    time_col='timestamp',
    target_col="value"
)
📘 Available models in Azure AI If you are using an Azure AI endpoint, please be sure to set model="azureai": nixtla_client.forecast(..., model="azureai") For the public API, we support two models: timegpt-1 and timegpt-1-long-horizon. By default, timegpt-1 is used. Please see this tutorial on how and when to use timegpt-1-long-horizon.
By default, only a small amount of finetuning is applied (finetune_depth=1). We can increase the intensity of finetuning by increasing the finetune_depth parameter. Note that increasing finetune_depth and finetune_steps increases wall time for generating predictions. 
# Read data
df = pd.read_csv("https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv")

# Forecast with fine-tuning.
# Here, we fine-tune for 5 steps
# and we finetune more than just the last layer
forecast_df = nixtla_client.forecast(
    df=df,
    h=12,
    finetune_steps=5,
    finetune_depth=2,
    time_col='timestamp',
    target_col="value"
)
For more information on fine-tuning, read our fine-tuning tutorial.