AI World 4 · ML Pipeline · Stage 1

Shape the Data

Clean without code, one feature group for training and scoring, a quality score on the catalogue, a 2 TB job that starts now, and data in the job's Region.

Data Wrangler and DataBrew
Feature Store
Input modes

Domain: AI and machine learning
World: AI World 4: ML Pipeline (Associate)
Game type: Solution board: Drop services into each row's slots, then run records through them.
Time: About 15 minutes on a first attempt (estimated)
Exam guides: AIF-C01Task 1.1: Explain basic AI concepts and terminologies; Task 1.3: Describe the AI/ML development lifecycle
MLA-C02Skill 1.1.2: Make storage decisions and configure storage services based on cost, performance, data structure, and data compliance; Skill 1.1.5: Ingest from and write by using appropriate data formats (for example, Apache Parquet, JSON, CSV, ORC) based on data access patterns; Skill 1.1.9: Ingest data into SageMaker Feature Store; Skill 1.2.1: Transform data by using AWS tools (for example, AWS Glue, AWS Glue DataBrew, Spark on Amazon EMR, SageMaker Data Wrangler); Skill 1.2.2: Create and manage features by using AWS tools (for example, SageMaker Feature Store); Skill 1.3.1: Validate data quality (for example, by using DataBrew, AWS Glue Data Quality); Skill 1.3.7: Clean data (for example, by detecting outliers, imputing missing data, deduplication)
AIP-C01Skill 1.3.2: Create data processing workflows to handle complex data types including text, image, audio, and tabular data with specialized processing requirements for FM consumption (for example, by using Amazon Bedrock multimodal models, SageMaker Processing, AWS Transcribe)