Useful Links
Computer Science
Artificial Intelligence
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
Operationalizing Pipelines
Automation and Orchestration
Pipeline Triggering
Scheduled Triggers
Cron-based Scheduling
Calendar-based Scheduling
Interval-based Scheduling
Event-based Triggers
Data Arrival Events
Model Performance Events
External System Events
Manual Triggers
On-demand Execution
Emergency Procedures
Testing and Debugging
Workflow Orchestration
Task Scheduling
Dependency Management
Resource Allocation
Error Recovery
Automation Patterns
Fully Automated Pipelines
Human-in-the-loop Automation
Conditional Automation
CI/CD for Machine Learning
Continuous Integration
Code Quality Checks
Linting and Formatting
Static Analysis
Security Scanning
Automated Testing
Unit Tests
Integration Tests
Data Quality Tests
Model Performance Tests
Build Automation
Artifact Generation
Dependency Management
Environment Setup
Continuous Delivery
Deployment Automation
Staging Deployment
Production Deployment
Multi-environment Management
Release Management
Release Planning
Rollback Procedures
Change Management
Continuous Training
Automated Retraining
Performance-based Triggers
Data-based Triggers
Time-based Triggers
Model Validation
Automated Testing
Performance Comparison
Business Validation
Pipeline Testing
Component Testing
Integration Testing
End-to-end Testing
Performance Testing
Version Control and Reproducibility
Code Versioning
Git Workflows
Feature Branching
GitFlow
GitHub Flow
Branching Strategies
Merge Strategies
Tag Management
Data Versioning
Data Version Control Tools
DVC
Pachyderm
LakeFS
Versioning Strategies
Snapshot-based Versioning
Delta-based Versioning
Content-addressable Storage
Data Lineage Tracking
Model Versioning
Model Registry Integration
Version Metadata
Model Lineage
Reproducibility Guarantees
Environment Versioning
Container Images
Dependency Management
Environment Snapshots
Experiment Management
Experiment Tracking
Parameter Logging
Metric Logging
Artifact Logging
Metadata Capture
Experiment Organization
Project Structure
Experiment Grouping
Tag Management
Search and Discovery
Reproducibility
Environment Capture
Seed Management
Deterministic Execution
Result Validation
Collaboration
Experiment Sharing
Result Comparison
Knowledge Transfer
Scalability and Performance
Distributed Processing
Data Parallelism
Model Parallelism
Pipeline Parallelism
Task Parallelism
Resource Management
Compute Resource Allocation
CPU Allocation
Memory Management
GPU Scheduling
Storage Management
Data Locality
Caching Strategies
Storage Optimization
Network Optimization
Bandwidth Management
Latency Optimization
Data Transfer Optimization
Performance Optimization
Bottleneck Identification
Performance Profiling
Optimization Strategies
Monitoring and Tuning
Auto-scaling
Horizontal Auto-scaling
Vertical Auto-scaling
Predictive Scaling
Cost-aware Scaling
Previous
4. Tools and Frameworks for ML Pipelines
Go to top
Next
6. Advanced Topics in ML Pipelines