Which Amazon SageMaker service is the best fit for this requirement?

A company stores its training datasets on Amazon S3 in the form of tabular data running into millions of rows. The company needs to prepare this data for Machine Learning jobs. The data preparation involves data selection, cleansing, exploration, and visualization using a single visual interface.

Which Amazon SageMaker service is the best fit for this requirement?
A . Amazon SageMaker Feature Store
B . Amazon SageMaker Data Wrangler
C . SageMaker Model Dashboard
D . Amazon SageMaker Clarify

View Answer

Answer: B

Explanation:

Correct option:

Amazon SageMaker Data Wrangler:

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare tabular and image data for ML from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow (including data selection, cleansing, exploration, visualization, and processing at scale) from a single visual interface. You can use SQL to select the data that you want from various data sources and import it quickly. Next, you can use the data quality and insights report to automatically verify data quality and detect anomalies, such as duplicate rows and target leakage. SageMaker Data Wrangler contains over 300 built-in data transformations, so you can quickly transform data without writing code.

With the SageMaker Data Wrangler data selection tool, you can quickly access and select your tabular and image data from various popular sources – such as Amazon Simple Storage Service (Amazon S3), Amazon Athena, Amazon Redshift, AWS Lake Formation, Snowflake, and Databricks – and over 50 other third-party sources – such as Salesforce, SAP, Facebook Ads, and Google Analytics. You can also write queries for data sources using SQL and import data directly into SageMaker from various file formats, such as CSV, Parquet, JSON, and database tables.

How Data Wrangler works:

via – https://aws.amazon.com/sagemaker/data-wrangler/

Incorrect options:

SageMaker Model Dashboard – Amazon SageMaker Model Dashboard is a centralized portal, accessible from the SageMaker console, where you can view, search, and explore all of the models in your account. You can track which models are deployed for inference and if they are used in batch transform jobs or hosted on endpoints.

Amazon SageMaker Clarify – SageMaker Clarify helps identify potential bias during data preparation without writing code. You specify input features, such as gender or age, and SageMaker Clarify runs an analysis job to detect potential bias in those features.

Amazon SageMaker Feature Store – Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference.

Reference: https://aws.amazon.com/sagemaker/data-wrangler/