From 6fd5d4e60c065f4436782cc2b27ce566ea383dc9 Mon Sep 17 00:00:00 2001 From: Pedro Pimentel Date: Tue, 3 Nov 2020 18:15:44 -0300 Subject: [PATCH 1/2] Update blog post link --- ml_ops/visualization_blog/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ml_ops/visualization_blog/README.md b/ml_ops/visualization_blog/README.md index b7c0cb1..b67e3b9 100644 --- a/ml_ops/visualization_blog/README.md +++ b/ml_ops/visualization_blog/README.md @@ -102,7 +102,7 @@ Due to how Amazon Forecast works, all strings provided on the Datasets are trans To perform visualizations on the forecasted data we recommend using Athena to query the tables already created by the Step Functions workflow. You can merge the tables __train__ and __forecast__ using basic SQL queries to get a unified view over your training and forecasted dataset. Further -instructions on how to visualize using Amazon QuickSight will be provided in the [AWS Blogpost](https://aws.amazon.com/blogs/machine-learning/). +instructions on how to visualize using Amazon QuickSight will be provided in the [AWS Blogpost](https://aws.amazon.com/blogs/machine-learning/building-ai-powered-forecasting-automation-with-amazon-forecast-by-applying-mlops/). ```sql SELECT LOWER(forecast.item_id) as item_id, From 3f84d9113b6e56ae230a3544cca8b5779634a90a Mon Sep 17 00:00:00 2001 From: Pedro Pimentel Date: Tue, 3 Nov 2020 18:16:02 -0300 Subject: [PATCH 2/2] Fix issue #92 --- ml_ops/visualization_blog/lambdas/updateresources/update.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ml_ops/visualization_blog/lambdas/updateresources/update.py b/ml_ops/visualization_blog/lambdas/updateresources/update.py index 6298513..b1e51ba 100644 --- a/ml_ops/visualization_blog/lambdas/updateresources/update.py +++ b/ml_ops/visualization_blog/lambdas/updateresources/update.py @@ -62,7 +62,7 @@ def create_table(table_name, attributes, input_path, delimiter=','): ) cursor = connect( - s3_staging_dir='s3://{}/stage', + s3_staging_dir='s3://{}/stage/ '.format(bucket), region_name=environ['AWS_REGION'], work_group=environ['ATHENA_WORKGROUP'] ).cursor()