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
Advanced Topics in ML Pipelines
Feature Stores
Architecture and Design
Centralized Feature Repository
Feature Serving Architecture
Real-time vs. Batch Features
Feature Computation Engine
Feature Management
Feature Definition
Feature Versioning
Feature Discovery
Feature Lineage
Consistency Guarantees
Training-Serving Consistency
Point-in-time Correctness
Feature Freshness
Data Quality Assurance
Integration Patterns
Pipeline Integration
Model Training Integration
Inference Integration
Monitoring Integration
Popular Feature Store Solutions
Feast
Tecton
Hopsworks
AWS Feature Store
Model Registry
Registry Architecture
Centralized Model Storage
Metadata Management
Version Control
Access Control
Model Lifecycle Management
Model Staging
Development Stage
Staging Stage
Production Stage
Archived Stage
Promotion Workflows
Approval Processes
Retirement Procedures
Model Governance
Model Approval Workflows
Compliance Tracking
Audit Trails
Policy Enforcement
Integration Capabilities
CI/CD Integration
Deployment Integration
Monitoring Integration
Experiment Tracking Integration
Real-time and Streaming Pipelines
Streaming Architecture Patterns
Lambda Architecture
Kappa Architecture
Unified Batch and Stream Processing
Real-time Data Processing
Stream Processing Frameworks
Event-driven Architecture
Message Queue Integration
Low-latency Processing
Online Feature Engineering
Real-time Transformations
Streaming Aggregations
Window Functions
State Management
Online Model Serving
Model Serving Infrastructure
Latency Optimization
Throughput Optimization
Caching Strategies
Stream Processing Challenges
Late Data Handling
Out-of-order Events
Exactly-once Processing
Fault Tolerance
Hybrid Pipeline Architectures
Batch-Stream Integration
Combining Batch and Real-time Components
Data Synchronization
Consistency Management
Performance Optimization
Multi-modal Pipelines
Text and Image Processing
Structured and Unstructured Data
Cross-modal Feature Engineering
Edge-Cloud Hybrid Systems
Edge Processing
Cloud Processing
Data Synchronization
Model Distribution
Use Case Patterns
Recommendation Systems
Fraud Detection
Predictive Maintenance
Real-time Analytics
Security and Governance
Data Security
Data Encryption
Encryption at Rest
Encryption in Transit
Key Management
Access Control
Role-based Access Control
Attribute-based Access Control
Fine-grained Permissions
Data Privacy
Data Anonymization
Differential Privacy
Privacy-preserving ML
Model Security
Model Protection
Adversarial Attack Prevention
Model Watermarking
Secure Model Serving
Pipeline Security
Secure Communication
Authentication and Authorization
Audit Logging
Vulnerability Management
Compliance and Governance
Regulatory Compliance
GDPR Compliance
HIPAA Compliance
Industry Standards
Governance Frameworks
Policy Management
Risk Assessment
Auditing and Monitoring
Audit Trail Management
Compliance Reporting
Security Monitoring
Incident Response
Cost Optimization
Cost Monitoring and Analysis
Resource Usage Tracking
Cost Attribution
Budget Management
Cost Forecasting
Resource Optimization Strategies
Right-sizing Resources
Resource Scheduling
Workload Optimization
Storage Optimization
Cloud Cost Management
Spot Instances and Preemptible VMs
Reserved Instances
Auto-scaling Policies
Multi-cloud Strategies
Pipeline Efficiency
Execution Optimization
Data Movement Optimization
Caching Strategies
Parallel Processing
Cost-Performance Trade-offs
Performance vs. Cost Analysis
SLA-driven Optimization
Business Value Optimization
Previous
5. Operationalizing Pipelines
Go to top
Back to Start
1. Fundamentals of ML Pipelines