AWS prescriptive-guidance documentation change
Summary
Updated branding from 'SageMaker AI Feature Store' to 'SageMaker Feature Store', added IAM documentation link, standardized example bucket name, and fixed typography.
Security assessment
The change adds a direct link to IAM roles documentation and maintains existing security content about granular access control, enhancing security documentation. However, there's no evidence of addressing a specific vulnerability. The bucket name change to 'DOC-EXAMPLE-BUCKET' is a standardization with no security impact.
Diff
diff --git a/prescriptive-guidance/latest/ml-operations-planning/feature-store.md b/prescriptive-guidance/latest/ml-operations-planning/feature-store.md index f929101b1..914f7f1cc 100644 --- a//prescriptive-guidance/latest/ml-operations-planning/feature-store.md +++ b//prescriptive-guidance/latest/ml-operations-planning/feature-store.md @@ -5 +5 @@ -[Documentation](/index.html)[AWS Prescriptive Guidance](https://aws.amazon.com/prescriptive-guidance/)[Planning for successful MLOps](welcome.html) +[Documentation](/index.html)[AWS Prescriptive Guidance](https://aws.amazon.com/prescriptive-guidance/)[Planning for successful MLOps](introduction.html) @@ -9 +9 @@ Use time travel queriesUse IAM rolesUse unit testing -# Feature store +# Feature Store @@ -11 +11 @@ Use time travel queriesUse IAM rolesUse unit testing -Using [SageMaker AI Feature Store](https://aws.amazon.com/sagemaker/feature-store/) increases team productivity, because it decouples component boundaries (for example, storage versus usage). It also provides feature reusability across different data science teams within your organization. +Using [SageMaker Feature Store](https://aws.amazon.com/sagemaker/feature-store/) increases team productivity, because it decouples component boundaries (for example, storage versus usage). It also provides feature reusability across different data science teams within your organization. @@ -15 +15 @@ Using [SageMaker AI Feature Store](https://aws.amazon.com/sagemaker/feature-stor -Time travel capabilities in Feature Store help reproduce model builds and support stronger governance practices. This can be useful when an organization wants to assess data lineage, similar to how version control tools such as Git assess code. Time travel queries also help organizations provide accurate data for compliance checks. For more information, see [Understanding the key capabilities of Amazon SageMaker AI Feature Store](https://aws.amazon.com/blogs/machine-learning/understanding-the-key-capabilities-of-amazon-sagemaker-feature-store/) on the AWS Machine Learning blog. +Time travel capabilities in Feature Store help reproduce model builds and support stronger governance practices. This can be useful when an organization wants to assess data lineage, similar to how version control tools such as Git assess code. Time travel queries also help organizations provide accurate data for compliance checks. For more information, see [Understanding the key capabilities of Amazon SageMaker Feature Store](https://aws.amazon.com/blogs/machine-learning/understanding-the-key-capabilities-of-amazon-sagemaker-feature-store/) on the AWS Machine Learning blog. @@ -19 +19 @@ Time travel capabilities in Feature Store help reproduce model builds and suppor -Feature Store also helps improve security without affecting team productivity and innovation. You can use AWS Identity and Access Management (IAM) roles to give or restrict granular access to specific features for specific users or groups. +Feature Store also helps improve security without affecting team productivity and innovation. You can use [AWS Identity and Access Management (IAM) roles](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles.html) to give or restrict granular access to specific features for specific users or groups. @@ -31 +31 @@ For example, the following policy restricts access to a sensitive feature in Fea - "Resource": "arn:aws:s3:::amzn-s3-demo-bucket--usw2-az1--x-s3/12345678910/sagemaker/us-east-2/offline-store/doctor-appointments" + "Resource": "arn:aws:s3:::DOC-EXAMPLE-BUCKET/12345678910/sagemaker/us-east-2/offline-store/doctor-appointments" @@ -36 +36 @@ For example, the following policy restricts access to a sensitive feature in Fea -For more information about data security and encryption using Feature Store, see [Security and access control](https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-security.html) in the SageMaker AI documentation. +For more information about data security and encryption using Feature Store, see [Security and access control](https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-security.html) in the SageMaker documentation. @@ -42 +42 @@ When data scientists create models based on some data, they often make assumptio -However, the distribution of this new data might have changed, which could affect the current algorithm's performance. An automated way to check for these types of issues is to borrow the concept of _unit testing_ from software engineering. Common things to test for include the percentage of missing values, the cardinality of categorical variables, and whether real valued columns adhere to some expected distribution by using a framework such as hypothesis test statistics ([_t_ -test](https://en.wikipedia.org/wiki/Student%27s_t-test)). You might also want to validate the data schema, to make sure it hasn’t changed and won’t generate invalid input features silently. +However, the distribution of this new data might have changed, which could affect the current algorithm's performance. An automated way to check for these types of issues is to borrow the concept of _unit testing_ from software engineering. Common things to test for include the percentage of missing values, the cardinality of categorical variables, and whether real valued columns adhere to some expected distribution by using a framework such as hypothesis test statistics ([_t_ -test](https://en.wikipedia.org/wiki/Student%27s_t-test)). You might also want to validate the data schema, to make sure it hasn't changed and won't generate invalid input features silently.