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
AIF-C01 · FoundationalAI Practitioner - Foundational
DEA-C01 · AssociateData Engineer - Associate
MLS-C01 · SpecialtyMachine Learning - Specialty
  • Week 1
    • 1.The ML Lifecycle (from a Specialty Perspective)
    • 2.Data Storage for ML: S3, EFS, FSx for Lustre, and Data Formats
    • 3.Data Ingestion: Kinesis, Glue, Batch vs. Streaming
    • 4.Data Labeling: SageMaker Ground Truth, Active Learning, Label Quality
    • 5.Week 1 Comprehensive Review: ML Overview & Data Engineering 1
  • Week 2
    • 1.Data Transformation and ETL: AWS Glue, Spark, and EMR
    • 2.Automating ML Training Pipelines: Step Functions and SageMaker Pipelines
    • 3.Data Augmentation and Synthesis: Addressing Insufficient and Imbalanced Data
    • 4.Data Storage and Access Optimization: Pipe vs File Mode, FSx for Lustre, Distributed Training
    • 5.Week 2 Comprehensive Review: From Transformation to Distributed Training Data Supply
  • Week 3
    • 1.Data Cleaning: Missing Values, Outlier Detection, Duplicates and Errors
    • 2.Feature Engineering: Scaling, Encoding, and Binning
    • 3.Time Series, Text Features, and High-Cardinality Categorical Handling
    • 4.SageMaker Tools: Data Wrangler, Processing Job, Feature Store
    • 5.Week 3 Comprehensive Review: Cleaning and Feature Engineering
  • Week 4
    • 1.Dimensionality Reduction: PCA, t-SNE, and the Curse of Dimensionality
    • 2.Feature Selection: Filter, Wrapper, Embedded, Importance, Multicollinearity
    • 3.Data Visualization: Distribution, Correlation, QuickSight, Insights
    • 4.Handling Class Imbalance: Over/Undersampling, SMOTE, Class Weights, Evaluation
    • 5.Week 4 Comprehensive Review: Dimensionality, Feature Selection, Visualization, Imbalance
  • Week 5
    • 1.Statistical Foundations: Distribution, Central Tendency, Dispersion, Transformations, Sample and Population
    • 2.Correlation and Relationships: Correlation Coefficients, Causation vs. Correlation, Multivariate Relationships
    • 3.Data Leakage: Causes, Detection, Prevention; Time Series Leakage; Target Leakage
    • 4.Validation Design: train/validation/test Split, Cross-Validation, Time Series Split, Stratified Sampling
    • 5.Week 5 Comprehensive Review: Statistics and Validation Design
  • Week 6
    • 1.Algorithm Selection: Problem Type to Mapping
    • 2.SageMaker Builtin 1: XGBoost, Linear Learner, K-Means, KNN
    • 3.SageMaker Builtin 2: Text, Image, Time Series, Recommendation
    • 4.Unsupervised/Anomaly Detection: RCF, PCA, IP Insights, Topic Models (LDA/NTM)
    • 5.Week 6 Comprehensive Review: Algorithm Selection and SageMaker Builtins
  • Week 7
    • 1.Neural Network Foundations: Perceptron to Backpropagation
    • 2.CNN: Convolutional Neural Networks and Computer Vision
    • 3.RNNs and Sequences: From LSTM to Transformer
    • 4.Learning Techniques and Transfer Learning
    • 5.Week 7 Synthesis: Deep Learning Summary
  • Week 8
    • 1.SageMaker Training Jobs: Estimator, Input Modes, Distributed Learning, Spot
    • 2.Hyperparameter Tuning (AMT): Bayesian, Random, Hyperband
    • 3.Overfitting/Underfitting: Diagnosis and Regularization/Data Augmentation
    • 4.Learning Optimization: Batch Size, Learning Rate, Gradient Issues, Debugger/Profiler
    • 5.Week 8 Review: Training, Tuning, Generalization
  • Week 9
    • 1.Classification Evaluation Metrics: Accuracy, Precision, Recall, F1 and Confusion Matrix
    • 2.ROC/AUC and Threshold Adjustment: Reading Model Performance with Curves
    • 3.Regression Evaluation Metrics: RMSE, MAE, MAPE, R² and Residual Analysis
    • 4.Model Debugging and Bias: SageMaker Debugger and Clarify
    • 5.Week 9 Review: Evaluation and Debugging
  • Week 10
    • 1.Inference Options: Real-time vs Serverless vs Asynchronous vs Batch Transform
    • 2.Real-time Endpoint Operations: Configuration, Auto Scaling, Multi-Model
    • 3.Inference Optimization: Neo, Elastic Inference, Inferentia, Inference Pipelines
    • 4.Deployment Strategies: A/B Testing, Blue/Green, Canary, Shadow, Rollback
    • 5.Week 10 Review: ML Implementation & Operations 1 — Deployment & Inference
  • Week 11
    • 1.Model Monitoring: SageMaker Model Monitor and Drift Response
    • 2.MLOps: SageMaker Pipelines, Model Registry, CI/CD
    • 3.ML Security: IAM Execution Roles, VPC Isolation, KMS Encryption
    • 4.Operations & Cost: Cost Optimization, Logging/Audit, Disaster Recovery
    • 5.Week 11 Review: Monitoring, MLOps, Security, Operations
  • Week 12
    • 1.Domains 1 & 2 Integration: Data Engineering + EDA
    • 2.Domain 3 Integration: Modeling (Algorithm to Evaluation)
    • 3.Domain 4 Integration: ML Implementation & Operations
    • 4.Synthesis: 4 Domains + End-to-End Scenarios
    • 5.D-Day Wrap-Up: Exam Structure, Time Allocation, Requirement Translation Tables, Trap Roundup
← MLS-C01/Week 10/Day 3
MLS-C01· AssociateWeek 10 · Day 3~10 min read

Day 3 - Inference Optimization: Neo, Elastic Inference, Inferentia, Inference Pipelines

Operating an endpoint is different from operating it efficiently. Unlike training costs, inference costs persist for the entire model lifespan, so optimization directly impacts total cost. SageMaker provides multiple tools to make models faster (lower latency), cheaper (lower cost), and smaller (lighter resource footprint). Today covers SageMaker Neo, Elastic Inference, AWS Inferentia, and throughput strategies like inference pipelines and batching.

SageMaker Neo: Train Once, Deploy Optimally Anywhere

Neo compiles and optimizes trained models for specific hardware (instances or edge devices)

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.

See ProLogin
PreviousReal-time Endpoint Operations: Configuration, Auto Scaling, Multi-ModelWeek 10 · Day 2Next Deployment Strategies: A/B Testing, Blue/Green, Canary, Shadow, RollbackWeek 10 · Day 4