This is a big open-ended question and I spent the first few minutes just trying to scope it properly.
Start by clarifying requirements: data sources, volume, velocity, variety, and ML use cases (training vs. inference). Then propose a high-level architecture using AWS services (e.g., Kinesis, Glue, S3, Redshift, SageMaker) and dive into trade-offs around batch vs. streaming, storage formats, and orchestration.
Pro tip: Emphasize data quality and lineage from the start—ML models are only as good as their data. Mention how you'd handle schema evolution and backfills without disrupting training pipelines.
Ask about data sources, volume, latency needs, and ML use cases (e.g., real-time inference vs. batch training). This scopes the design and shows you avoid assumptions.
Choose AWS services for ingestion: Kinesis for streaming, AWS Glue or DMS for batch, and S3 as the landing zone. Discuss handling different data formats and frequencies.
Outline transformation steps using Glue/EMR/Lambda, and storage layers (S3 for raw/processed, Redshift/S3 for analytics). Highlight partitioning, columnar formats (Parquet), and cataloging with Glue Catalog.
Use AWS Step Functions or MWAA (Managed Workflows for Apache Airflow) to schedule and monitor pipelines. Include error handling, retries, and alerting.
Explain how processed data feeds into SageMaker for training and inference, including feature stores, versioning, and monitoring for data drift.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.