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Machine Learning
Machine Learning Pipelines
1. Fundamentals of ML Pipelines
2. Core Stages of an ML Pipeline
3. Designing and Building ML Pipelines
4. Tools and Frameworks for ML Pipelines
5. Operationalizing Pipelines
6. Advanced Topics in ML Pipelines
Designing and Building ML Pipelines
Architectural Principles
Modularity and Componentization
Single Responsibility Principle
Loose Coupling
High Cohesion
Interface Design
Reusability of Components
Component Libraries
Parameterized Components
Template-based Components
Cross-project Reuse
Configurability and Parameterization
Configuration Management
Configuration Files
Environment Variables
Runtime Parameters
Parameter Validation
Default Value Management
Idempotency
Ensuring Repeatable Results
Stateless Processing
Side Effect Management
Deterministic Execution
Error Handling and Resilience
Graceful Degradation
Retry Mechanisms
Circuit Breaker Pattern
Fallback Strategies
Scalability Patterns
Horizontal Scaling
Vertical Scaling
Load Distribution
Resource Optimization
Pipeline Structure and Design
Directed Acyclic Graphs
Graph Theory Fundamentals
Node Types
Data Processing Nodes
Model Training Nodes
Evaluation Nodes
Decision Nodes
Edge Relationships
Data Dependencies
Control Dependencies
Conditional Dependencies
Graph Optimization
Parallel Execution
Resource Allocation
Critical Path Analysis
Pipeline Topologies
Linear Pipelines
Branching Pipelines
Converging Pipelines
Cyclic Patterns
Conditional Execution
Conditional Branches
Dynamic Pipeline Generation
Runtime Decision Making
Pipeline Composition
Sub-pipeline Design
Pipeline Nesting
Pipeline Inheritance
Pipeline as Code
Programmatic Pipeline Definition
Scripting Approaches
Domain-Specific Languages
Configuration-driven Pipelines
Template-based Generation
Version Control Integration
Pipeline Code Management
Change Tracking
Branching Strategies
Merge Conflict Resolution
Testing Pipeline Code
Unit Testing Components
Integration Testing
End-to-end Testing
Performance Testing
Documentation and Maintenance
Code Documentation
Pipeline Documentation
Maintenance Procedures
Artifact and Metadata Management
Pipeline Artifacts
Data Artifacts
Raw Data
Processed Datasets
Feature Sets
Data Samples
Model Artifacts
Trained Models
Model Checkpoints
Model Configurations
Model Metrics
Evaluation Artifacts
Performance Reports
Visualizations
Test Results
Benchmark Comparisons
Metadata Management
Metadata Types
Descriptive Metadata
Structural Metadata
Administrative Metadata
Metadata Stores
Centralized Repositories
Distributed Storage
Query Interfaces
Lineage Tracking
Data Lineage
Model Lineage
Code Lineage
Experiment Lineage
Artifact Versioning
Versioning Strategies
Semantic Versioning
Content-based Versioning
Time-based Versioning
Storage Solutions
File-based Storage
Database Storage
Object Storage
Distributed Storage Systems
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2. Core Stages of an ML Pipeline
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4. Tools and Frameworks for ML Pipelines