COMP 3211 · Machine Learning on AWS: Data and Modelling

Led by Demingian Quality Simulacrum

About this course

Where is the data? What format? How does it flow from raw storage to training? SageMaker separates development (notebook) from training (managed job on any instance, billed by the second, automatically shut down). Glue and Data Wrangler prepare it. Feature Store centralises it.

This course covers the data and modelling side of production ML on AWS, based on AWS documentation and industry MLOps practice.

1. Module 1

   Data Formats and Storage

   Led by Demingian Quality Simulacrum

   The question

   Parquet scans only the columns needed. JSON scans everything. RecordIO is SageMaker's native training format. For a 100GB transaction dataset powering monthly model training, ad-hoc queries, and real-time streaming — which format for which use case?

   Outcome

   The student can select data formats for ML workloads and explain the advantage of columnar formats.

   Sub-units

   1. ○ 1.1 Choose a Format Open this unit → ✓ ↻
   Begin the course → Print certificate of completion
2. Module 2

   ETL with AWS Glue

   Led by Demingian Quality Simulacrum

   The question

   Glue for code-based ETL, DataBrew for visual preparation, Data Wrangler for ML-specific transformations. Raw clickstream JSON → SageMaker-ready Parquet: which AWS tool handles each stage?

   Outcome

   The student can trace data through the Glue/DataBrew/Data Wrangler pipeline.

   Sub-units

   1. ○ 2.1 Design an ETL Pipeline Open this unit → ✓ ↻
   Open this module → Print certificate of completion
3. Module 3

   ML Model Development with SageMaker

   Led by Demingian Quality Simulacrum

   The question

   SageMaker training jobs: spin up an instance, run training, store artifacts in S3, shut down. You pay only for training time. What is the lifecycle — and when should you use a built-in algorithm vs bring your own container?

   Outcome

   The student can describe the SageMaker training job lifecycle and select between built-in and custom algorithms.

   Sub-units

   1. ○ 3.1 Training Job Design Open this unit → ✓ ↻
   Open this module → Print certificate of completion
4. Module 4

   Advanced ML Paradigms in SageMaker

   Led by Demingian Quality Simulacrum

   The question

   XGBoost corrects errors sequentially. LightGBM uses histogram-based splits to process large datasets faster. Exclusive feature bundling reduces sparse feature dimensions. When does LightGBM beat XGBoost — and why do both usually beat random forests on tabular data?

   Outcome

   The student can explain gradient boosting and identify when LightGBM vs XGBoost is appropriate.

   Sub-units

   1. ○ 4.1 Gradient Boosting in Plain Terms Open this unit → ✓ ↻
   Open this module → Print certificate of completion
5. Module 5

   Data Quality and Feature Store

   Led by Demingian Quality Simulacrum

   The question

   A model that was 82% accurate at launch is now 67%. No code has changed. List five hypotheses — all involving data. How do you test each one? What would a Feature Store have prevented?

   Outcome

   The student can describe data drift, training-serving skew, and five failure modes for production ML data.

   Sub-units

   1. ○ 5.1 Final Essay: What Can Go Wrong with Production Data? Open this unit → ✓ ↻
   Open this module → Print certificate of completion