May 23, 2024 - Githubissues

In BigQuery ML univariate time series models, the FORECAST_LIMIT_LOWER_BOUND and FORECAST_LIMIT_UPPER_BOUND parameters now work with the TIME_SERIES_ID_COL parameter. The FORECAST_LIMIT_LOWER_BOUND and FORECAST_LIMIT_UPPER_BOUND arguments let you set the lower and upper bounds of the forecasted values returned by the model. Try this feature with the Limit forecasted values for a time series model tutorial.

BigQuery ML now offers the following Generative AI features:

Grounding and safety attributes when you use Vertex AI Gemini models with the ML.GENERATE_TEXT function:
- Use the ground_with_google_search argument to perform grounding. Grounding lets the Gemini model use additional information from the internet when generating a response, in order to make model responses more specific and factual.
- Use the safety_settings argument to configure safety attributes.The Gemini model filters the responses it returns based on the attributes you specify.
Video embedding ( Preview). You can use the ML.GENERATE_EMBEDDING function with a remote model based on a Vertex AI multimodalembedding model to create multimodal embeddings that include video embeddings.

To try the new video embedding functionality, see Generate video embeddings by using the ML.GENERATE_EMBEDDING function.

kitta65 / bq2cst