Cert Notes/ Commute Study Notes
Roadmap
KOEN
CLF-C02 · FoundationalCloud Practitioner - Foundational
DVA-C02 · AssociateDeveloper - Associate
SAA-C03 · AssociateSolutions Architect - Associate
SOA-C02 · AssociateCloudOps Engineer - Associate
SAP-C02 · ProfessionalSolutions Architect - Professional
DOP-C02 · ProfessionalDevOps Engineer - Professional
SCS-C03 · SpecialtySecurity - Specialty
MLA-C01 · AssociateMachine Learning Engineer - Associate
  • Week 1
    • 1.ML Lifecycle and the Role of ML Engineers
    • 2.ML Problem Types and Evaluation Metrics Basics
    • 3.Day 3
    • 4.SageMaker Overview: Studio, Training/Inference, Built-in Algorithms
    • 5.Week 1 Comprehensive Review — ML Fundamentals & AWS Stack
  • Week 2
    • 1.Data Collection: S3 Data Lake, Kinesis, Batch Ingestion, Data Formats
    • 2.Data Catalog & ETL: AWS Glue and DataBrew
    • 3.Query & Exploration: Athena, Redshift, EDA Basics
    • 4.Data Storage Strategy: Partitioning, Format Optimization, Training Readiness
    • 5.Week 2 Comprehensive Review — Data Collection & Storage Recap
  • Week 3
    • 1.Feature Engineering: The Art of Transforming Data into Numbers Models Can Read
    • 2.SageMaker Data Wrangler: No-Code Data Preparation
    • 3.SageMaker Feature Store: Managing Features as Assets
    • 4.Data Bias·Quality: Clarify, Class Imbalance Handling, Data Split
    • 5.Week 3 Comprehensive Review — Feature Engineering·Data Quality
  • Week 4
    • 1.SageMaker Training Job: Estimator, Input Channels, Instances, Spot
    • 2.Built-in Algorithms: XGBoost, Linear Learner, Image·Text, Input Formats
    • 3.Hyperparameter Tuning (AMT): Bayesian·Random·Grid, Early Stopping, Warm Start
    • 4.JumpStart·Pre-trained Models·Transfer Learning + Training Cost Optimization
    • 5.Week 4 Comprehensive Review: Model Development 1 — SageMaker Training
  • Week 5
    • 1.Custom Training: Script Mode, BYOC, Framework Containers
    • 2.Distributed Training: Data Parallel and Model Parallel
    • 3.Debugging and Profiling: SageMaker Debugger and Profiler
    • 4.Model Evaluation: Metric Selection, Overfitting, Cross-validation, Confusion Matrix
    • 5.Week 5 Comprehensive: Model Development 2 Review
  • Week 6
    • 1.Inference Options Overview: 4 Deployment Modes and Selection Criteria
    • 2.Real-time Endpoints: Configuration, Auto-scaling, Instance Selection
    • 3.Cost & Advanced Inference: Multi-model, Multi-container, Inference Pipeline, Inferentia
    • 4.Batch & Serverless Inference Deep Dive: Throughput Tuning & Cost Tradeoffs
    • 5.Week 6 Synthesis: Inference Deployment Review
  • Week 7
    • 1.Declare parameters, injectible at execution
    • 2.Create package group bundling models of same purpose
    • 3.buildspec.yml — commands CodeBuild executes
    • 4.CloudFormation: SageMaker endpoint declared as code
    • 5.Synthesis code: pipeline end with condition passes → approval triggers deploy
  • Week 8
    • 1.SageMaker Model Monitor: Data Quality and Model Quality Drift
    • 2.Bias and Explainability Drift: Monitoring During Operations with Clarify
    • 3.Filter recent errors from log group
    • 4.Day 4
    • 5.Best practice: aggregating operational metrics + model monitor metrics on one dashboard
  • Week 9
    • 1.Day 1
    • 2.Day 2
    • 3.Data and Model Protection: KMS Encryption and Secrets
    • 4.Day 4
    • 5.Day 5
  • Week 10
    • 1.Day 1
    • 2.Day 2
    • 3.Day 3
    • 4.Day 4
    • 5.Day 5
AIF-C01 · FoundationalAI Practitioner - Foundational
DEA-C01 · AssociateData Engineer - Associate
MLS-C01 · SpecialtyMachine Learning - Specialty
← MLA-C01/Week 3/Day 2
MLA-C01· AssociateWeek 3 · Day 2~17 min read

Day 2 - SageMaker Data Wrangler: No-Code Data Preparation

Doing scaling, encoding, and missing value handling from yesterday's lessons in pandas every time is tedious and makes transformation sequences hard to reproduce. SageMaker Data Wrangler bundles this entire flow into a visual interface. Load data, see distribution visually, stack transformations by clicking, and export to the training pipeline—all on one screen.

In the MLA-C01 exam, Data Wrangler often appears as the answer to scenarios needing "fast visual exploratory analysis and transformation." Today we cover four stages: connecting data sources, transforming, analyzing, and exporting.

What is Data Wrangler?

Data Wrangler is a data preparation tool running inside SageMaker Studio. The core concept is flow

The rest is Pro only

Week 1 is free for everyone. Week 2 onwards — plus mock exams and unlimited review — is included in the Pro plan.

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PreviousFeature Engineering: The Art of Transforming Data into Numbers Models Can ReadWeek 3 · Day 1Next SageMaker Feature Store: Managing Features as AssetsWeek 3 · Day 3