# AWS terminology

# Overall domain

1. ```python
    [Data Sensitivity Keywords]
    Keyword               → AWS Approach
    ---------------------------------------------
    "Sensitive info"      → Encryption (S3, KMS)
    "Personal data"       → Anonymization, Masking
    "HIPAA compliance"    → HIPAA-eligible services
    "PII protection"      → Data anonymization
    "Confidential data"   → Encryption at rest/transit
    
    [Performance Keywords]
    Keyword               → AWS Solution
    ---------------------------------------------
    "Real-time inference" → SageMaker Endpoints
    "Low latency"         → Serverless, Edge deployment
    "Scalable predictions"→ Auto-scaling inference
    "High throughput"     → Distributed training
    "Complex computations"→ Managed Spot Training
    
    [Model Complexity Keywords]
    Keyword               → AWS Approach
    ---------------------------------------------
    "Imbalanced dataset"  → Class weighting
    "Feature engineering" → SageMaker Feature Store
    "Model interpretability" → SageMaker Clarify
    "Drift detection"     → Model Monitor
    "Bias mitigation"     → Fairness metrics
    
    [Cost Optimization Keywords]
    Keyword               → AWS Solution
    ---------------------------------------------
    "Reduce training cost"→ Spot Instances
    "Efficient computing" → SageMaker managed spots
    "Resource optimization"→ Auto-scaling
    "Minimize infrastructure"→ Serverless options
    
    [Deployment Strategy Keywords]
    Keyword               → Deployment Type
    ---------------------------------------------
    "Zero downtime"       → Blue/Green deployment
    "Gradual rollout"     → Canary deployment
    "Risk mitigation"     → Shadow deployment
    "A/B testing"         → Traffic shifting
    "Minimal disruption"  → Incremental deployment
    
    [Data Processing Keywords]
    Keyword               → AWS Service
    ---------------------------------------------
    "Large-scale ETL"     → AWS Glue
    "Data lake"           → S3 + Glue
    "Complex transformations" → SageMaker Processing
    "Data catalog"        → AWS Glue Data Catalog
    "Cross-source integration" → Step Functions
    
    [Model Training Keywords]
    Keyword               → Training Approach
    ---------------------------------------------
    "Transfer learning"   → Pre-trained models
    "Domain adaptation"   → Fine-tuning
    "Custom algorithms"   → Bring Your Own Model (BYOM)
    "Hyperparameter tuning"→ SageMaker Hyperparameter Tuning
    "Few training samples"→ Few-shot learning techniques
    ```
    
2. # SageMaker domains:
    

```python
[Notebook Instance Keywords]
Keyword               → Interpretation
---------------------------------------------
"Collaborative ML dev"→ Jupyter notebook
"Shared environment"  → Managed notebook instance
"Reproducible research"→ Persistent workspace
"ML experimentation"  → Pre-configured environment

[Training Job Keywords]
Keyword               → Interpretation
---------------------------------------------
"Model learning"      → SageMaker Training Job
"Distributed training"→ Parallel processing
"Large dataset"       → Managed training infrastructure
"Complex algorithms"  → Scalable training

[Hyperparameter Tuning Keywords]
Keyword               → Interpretation
---------------------------------------------
"Optimal parameters"  → Hyperparameter Tuning Job
"Automated optimization"→ HPO Job
"Model performance"   → Hyperparameter search
"Efficient tuning"    → Bayesian optimization

[Inference & Deployment Keywords]
Keyword               → Interpretation
---------------------------------------------
"Real-time predictions"→ SageMaker Endpoint
"Offline processing"  → Batch Transform
"Edge device deploy"  → Edge Manager
"Hardware optimization"→ Neo
"Inference acceleration"→ Elastic Inference
"Endpoint configuration"→ Inference Recommender

[Data Preparation Keywords]
Keyword               → Interpretation
---------------------------------------------
"Data labeling"       → Ground Truth
"Visual data prep"    → Data Wrangler
"Feature management"  → Feature Store
"Reproducible features"→ Feature Store versioning

[Model Monitoring Keywords]
Keyword               → Interpretation
---------------------------------------------
"Model drift"         → Model Monitor
"Bias detection"      → Clarify
"Prediction explanation"→ Clarify
"Automated ML"        → Autopilot
"Model quality"       → Model Monitor metrics

[Advanced ML Keywords]
Keyword               → Interpretation
---------------------------------------------
"Automated model dev" → Autopilot
"Cross-device deploy" → Edge Manager
"Custom hardware"     → Neo
"Inference optimization"→ Elastic Inference
```

# 3 Real-time Processing, Batch Processing, and Storage/Database domains:

```python
[Real-time Processing Keywords]
Keyword               → Service/Interpretation
---------------------------------------------
"Streaming data"      → Kinesis Data Streams
"Data ingestion"      → Kinesis Data Firehose
"Real-time analytics" → Kinesis Data Analytics
"Distributed messaging"→ Amazon MSK
"Continuous data flow"→ Kinesis Services

[Batch Processing Keywords]
Keyword               → Service/Interpretation
---------------------------------------------
"ETL transformations" → AWS Glue
"Big data processing" → Amazon EMR
"SQL querying"        → Amazon Athena
"Data workflow"       → AWS Data Pipeline
"Distributed computing"→ Hadoop/Spark

[Storage Keywords]
Keyword               → Service/Interpretation
---------------------------------------------
"Model storage"       → Amazon S3
"Large dataset"       → S3 scalable storage
"Shared workspace"    → Amazon EFS
"High-performance file"→ Amazon FSx
"Scalable storage"    → S3 intelligent tiering

[Database Keywords]
Keyword               → Service/Interpretation
---------------------------------------------
"Structured data"     → Amazon RDS
"Relational database" → RDS (MySQL, PostgreSQL)
"NoSQL storage"       → DynamoDB
"Key-value storage"   → DynamoDB
"Caching layer"       → ElastiCache
"In-memory processing"→ ElastiCache

[Advanced Processing Indicators]
Keyword               → Complex Processing Need
---------------------------------------------
"Unstructured data"   → Consider Glue/EMR
"Time-series data"    → Kinesis/MSK
"Complex transformations"→ EMR/Glue
"Low-latency access"  → ElastiCache
"Scalable data store" → S3/DynamoDB
```

4. # ML Infrastructure domain
    

```python
[ML Infrastructure Keywords]
Keyword               → Service/Interpretation
---------------------------------------------
"Pre-configured environment" → Deep Learning AMIs
"ML-ready compute"    → EC2 ML Instances
"Optimized frameworks"→ Deep Learning Containers
"Custom ML hardware"  → AWS Inferentia
"Model optimization" → AWS Neuron SDK

[Hardware Optimization Keywords]
Keyword               → Optimization Approach
---------------------------------------------
"High-performance inference" → Inferentia Chip
"Model acceleration"  → Neuron SDK
"Low-latency ML"      → Custom ML hardware
"Efficient computing" → Optimized containers

[Deployment Readiness Keywords]
Keyword               → Infrastructure Capability
---------------------------------------------
"ML framework support"→ Deep Learning Containers
"Scalable ML compute" → EC2 ML Instances
"Quick start ML dev"  → Pre-configured AMIs
"Framework flexibility"→ Container-based solutions

[Performance Indicators]
Keyword               → Infrastructure Consideration
---------------------------------------------
"Complex model deployment" → Deep Learning Containers
"Hardware-specific optimization" → Neuron SDK
"Consistent ML environment" → Standardized AMIs
"Reproducible ML setup"    → Containerized frameworks
```

# 5.ML Development Domain

```python
[Model Training Keywords]
Keyword               → Interpretation/Concept
---------------------------------------------
"Data splitting"      → Training/Validation/Test Sets
"Model generalization"→ Cross-validation
"Prevent overfitting" → Regularization
"Model tuning"        → Hyperparameters
"Error measurement"   → Loss Functions

[Model Optimization Keywords]
Keyword               → Optimization Concept
---------------------------------------------
"Parameter optimization"→ Gradient Descent
"Model learning rate"  → Optimization algorithm
"Non-linear transformation"→ Activation Functions
"Model complexity"    → Bias/Variance Tradeoff

[Performance Evaluation Keywords]
Keyword               → Evaluation Metric
---------------------------------------------
"Classification accuracy"→ Confusion Matrix
"Model prediction quality"→ Precision/Recall/F1 Score
"Binary classification"→ ROC/AUC Curve
"Prediction error"    → RMSE/MAE Metrics

[Advanced ML Concept Indicators]
Keyword               → Complex ML Consideration
---------------------------------------------
"Model complexity management"→ Bias/Variance Balance
"Performance trade-offs"→ Regularization Techniques
"Prediction reliability"→ Cross-validation Strategies
"Error minimization"  → Loss Function Selection

[Detection and Evaluation Keywords]
Keyword               → Diagnostic Approach
---------------------------------------------
"Model performance"   → Confusion Matrix Analysis
"Classification accuracy"→ Precision/Recall Metrics
"Model discrimination"→ ROC/AUC Curve
"Regression accuracy" → RMSE/MAE Evaluation
```

# MLOps Domain

6. ```python
    [Model Lifecycle Management Keywords]
    Keyword               → MLOps Concept/Approach
    ---------------------------------------------
    "Model tracking"      → Model Versioning
    "Comparative analysis"→ A/B Testing
    "Centralized storage" → Model Registry
    "Workflow automation"→ ML Pipeline
    "Performance tracking"→ Model Monitoring
    "Deployment safety"   → Deployment Strategies
    
    [Deployment Strategy Keywords]
    Keyword               → Deployment Technique
    ---------------------------------------------
    "Minimal risk rollout"→ Blue/Green Deployment
    "Gradual introduction"→ Canary Deployment
    "Zero downtime"       → Incremental Deployment
    "Performance comparison"→ Shadow Deployment
    "Traffic management"  → Staged Rollout
    
    [Advanced MLOps Indicators]
    Keyword               → Complex MLOps Consideration
    ---------------------------------------------
    "Model reproducibility"→ Versioning & Tracking
    "Continuous improvement"→ Model Monitoring
    "Automated workflows" → ML Pipeline Orchestration
    "Risk mitigation"     → Deployment Strategies
    
    [Performance Management Keywords]
    Keyword               → Monitoring Approach
    ---------------------------------------------
    "Model drift detection"→ Performance Tracking
    "Prediction quality"  → Model Monitoring
    "Automated governance"→ Model Registry
    "Experiment tracking" → Versioning Strategies
    
    [Comparative Analysis Keywords]
    Keyword               → Evaluation Technique
    ---------------------------------------------
    "Model comparison"    → A/B Testing
    "Performance benchmark"→ Experimental Validation
    "Iterative improvement"→ Continuous Deployment
    "Version comparison"  → Model Registry Analysis
    ```
    
    7. # Security and Governance in ML domain
        
    
    ```python
    [Access Control Keywords]
    Keyword               → Security Concept
    ---------------------------------------------
    "Resource permissions"→ IAM Roles
    "Least privilege"     → IAM Policy
    "Identity management" → IAM Authentication
    "Restricted access"   → IAM Roles
    
    [Encryption Keywords]
    Keyword               → Security Mechanism
    ---------------------------------------------
    "Data protection"     → AWS KMS
    "Encryption key mgmt" → Key Management Service
    "Sensitive data"      → Encryption at rest/transit
    "Compliance"          → Encryption standards
    
    [Network Security Keywords]
    Keyword               → Network Protection
    ---------------------------------------------
    "Network isolation"   → VPC
    "Subnet segmentation" → VPC Subnets
    "Traffic control"     → Security Groups
    "Network protection"  → Network ACLs
    
    [Governance Keywords]
    Keyword               → Governance Approach
    ---------------------------------------------
    "Model tracking"      → Model Governance
    "Compliance workflow" → Approval Processes
    "Audit trail"         → Version Control
    "Reproducibility"     → Governance Frameworks
    
    [Data Management Keywords]
    Keyword               → Data Tracking
    ---------------------------------------------
    "Data source tracking"→ Data Lineage
    "Transformation log"  → Metadata Management
    "Data provenance"     → Audit Trail
    "End-to-end tracking" → Comprehensive Lineage
    
    [Compliance Indicators]
    Keyword               → Compliance Consideration
    ---------------------------------------------
    "Regulatory requirements"→ Governance Frameworks
    "Audit readiness"     → Comprehensive Tracking
    "Risk mitigation"     → Security Controls
    "Transparent ML"      → Detailed Lineage
    ```
    
    8. # ML Best Practices:
        
    
    ```python
    [Data Quality Keywords]
    Keyword               → Quality Approach
    ---------------------------------------------
    "Data cleansing"      → Preprocessing techniques
    "Representative data" → Balanced datasets
    "Outlier handling"    → Statistical methods
    "Data integrity"      → Validation processes
    
    [Feature Engineering Keywords]
    Keyword               → Feature Creation Strategy
    ---------------------------------------------
    "Meaningful predictors"→ Feature selection
    "Dimensionality reduction"→ Feature extraction
    "Domain knowledge"    → Expert-driven features
    "Complex relationships"→ Non-linear transformations
    
    [Model Evaluation Keywords]
    Keyword               → Evaluation Technique
    ---------------------------------------------
    "Comprehensive testing"→ Cross-validation
    "Performance metrics" → Holistic assessment
    "Model generalization"→ Validation strategies
    "Comprehensive analysis"→ Thorough model testing
    
    [Production Monitoring Keywords]
    Keyword               → Monitoring Approach
    ---------------------------------------------
    "Performance tracking"→ Continuous monitoring
    "Drift detection"     → Model performance metrics
    "Degradation prevention"→ Proactive monitoring
    "Real-time insights"  → Performance dashboards
    
    [Cost Optimization Keywords]
    Keyword               → Resource Efficiency
    ---------------------------------------------
    "Resource management"→ Efficient compute allocation
    "Cost-effective ML"   → Optimization techniques
    "Scalable infrastructure"→ Dynamic resource scaling
    "Minimal waste"       → Efficient model training
    
    [Reproducibility Keywords]
    Keyword               → Consistency Approach
    ---------------------------------------------
    "Consistent environments"→ Containerization
    "Experiment tracking" → Version control
    "Repeatable results"  → Deterministic processes
    "Workflow standardization"→ ML pipeline consistency
    
    [Advanced Best Practice Indicators]
    Keyword               → Sophisticated ML Consideration
    ---------------------------------------------
    "End-to-end quality"  → Comprehensive ML lifecycle
    "Continuous improvement"→ Iterative refinement
    "Strategic ml development"→ Holistic approach
    "Performance optimization"→ Advanced techniques
    ```