- Lab
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Libraries: If you want this lab, consider one of these libraries.
- Cloud
Organize ML Datasets and Model Artifacts in Amazon S3
In this lab, you will take on the role of a Machine Learning Engineer addressing challenges related to inconsistent dataset organization, missing version control, and difficulty in managing model artifacts. You will implement a structured approach in Amazon S3 to organize datasets and store model artifacts using clear naming conventions and versioning strategies. By associating models with relevant metadata, you will enable traceability, reproducibility, and easier reuse, aligning with real-world ML workflow and governance best practices.
Lab Info
Table of Contents
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Challenge
Organize ML Datasets in Amazon S3 to Support Repeatable Model Development
a. Create a structured S3 folder hierarchy for ML workflows b. Store datasets in formats and locations suited for ML access c. Apply dataset versioning and naming conventions
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Challenge
Manage Model Artifacts and Metadata in Amazon S3 for Traceability and Reuse
a. Store model artifacts using structured conventions b. Associate model artifacts with metadata c. Navigate S3 structure to locate a specific model version
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Challenge
Verify End-to-End Organization for ML Workflow Reuse
a. Review dataset and model structure for consistency b. Identify which dataset version was used for a specific model c. Confirm readiness for downstream ML workflows
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