Anomaly detection in production - google-cloud-platform

Question

I am trying to search for suggestions and solutions, but I am unable to find any.
After reading blogs, I am able to build a time series anomaly detection using BigQuery ML (Arima Plus).
My question is: how do I put such a model in production?
Probably I need to:
program the re-training of the model every X days
check whether there are new anomalies on the object table every X hours
record those anomalies in another table
But I also accept other suggestion on how to proceed.
Is there anyone out there that can give me any hint?
Thank you!

Answer 1

The best way I found is to create "scheduled queries":
schedule a query for re-training of the model every X days:
CREATE OR REPLACE MODEL mymodel
OPTIONS( model_type='arima_plus',
TIME_SERIES_DATA_COL='events',
TIME_SERIES_TIMESTAMP_COL='approx_hour',
HOLIDAY_REGION = 'GLOBAL',
CLEAN_SPIKES_AND_DIPS = FALSE,
DECOMPOSE_TIME_SERIES=TRUE)
AS (SELECT 
TIMESTAMP_TRUNC( PARSE_TIMESTAMP('%Y-%m-%dT%H:%M:%E*SZ',start_time), hour) as approx_hour, 
COUNT(1) AS events 
FROM  `mytable`
GROUP BY approx_hour);
 
schedule a query to perform anomaly detection on the latest events, and eventually write them on a table:
insert into `events_anomalies_table`
SELECT approx_hour as hour,
cast(events as int64) as actual_events,
cast(lower_bound as int64) as expected_min_events,
cast(upper_bound as int64) as expected_max_events,
current_timestamp() as execution_timestamp
FROM ML.DETECT_ANOMALIES(
MODEL`my_model`,
STRUCT (0.98 AS anomaly_prob_threshold),
( SELECT
TIMESTAMP_TRUNC( PARSE_TIMESTAMP('%Y-%m-%dT%H:%M:%E*SZ',start_time), hour) as approx_hour, 
COUNT(1) AS events 
FROM  `my_table`
WHERE PARSE_TIMESTAMP('%Y-%m-%dT%H:%M:%E*SZ',start_time) > TIMESTAMP_SUB(CURRENT_TIMESTAMP() , INTERVAL 1 HOUR)
GROUP BY approx_hour
LIMIT 1))
WHERE is_anomaly = True