关于“Movie Recommendations in BigQuery ML 2.5”的评价

GD S. · 已于 over 3 years前审核

Nicholas R. · 已于 over 3 years前审核

Asha Tejaswini G. · 已于 over 3 years前审核

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King E. · 已于 over 3 years前审核

John D. · 已于 over 3 years前审核

Charlene B. · 已于 over 3 years前审核

some simple big query can not be processed... a bit disappointing: SELECT AVG(predicted_rating) as average_rating, movieId FROM ML.RECOMMEND(MODEL `cloud-training-prod-bucket.movies.movie_recommender`) group by movieId order by movieId LIMIT 100000 Query exceeded resource limits. This query used 24249 CPU seconds but would charge only 43M Analysis bytes. This exceeds the ratio supported by the on-demand pricing model. Please consider moving this workload to the flat-rate reservation pricing model, which does not have this limit. 24249 CPU seconds were used, and this query must use less than 11000 CPU seconds.

Jianing M. · 已于 over 3 years前审核

Kamran S. · 已于 over 3 years前审核

Peter C. · 已于 over 3 years前审核

Daniel L. · 已于 over 3 years前审核

Aditya P. · 已于 over 3 years前审核

Francesc G. · 已于 over 3 years前审核

SELECT * FROM ML.PREDICT(MODEL `cloud-training-prod-bucket.movies.movie_recommender`, ( WITH allUsers AS ( SELECT DISTINCT userId FROM movies.movielens_ratings ) SELECT 96481 AS movieId, ( SELECT title FROM movies.movielens_movies WHERE movieId=96481) title, userId FROM allUsers )) ORDER BY predicted_rating DESC LIMIT 100 it's not working properly with the error Scalar subquery produced more than one element I had to change with SELECT * FROM ML.PREDICT(MODEL `cloud-training-prod-bucket.movies.movie_recommender`, ( WITH allUsers AS ( SELECT DISTINCT userId FROM movies.movielens_ratings ) SELECT 96481 AS movieId, ( SELECT DISTINCT title FROM movies.movielens_movies WHERE movieId=96481) title, userId FROM allUsers )) ORDER BY predicted_rating DESC LIMIT 100

Oussama B. · 已于 over 3 years前审核

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Siva C. · 已于 over 3 years前审核