UsefulLinks
Computer Science
Data Science
Data Science
1. Foundations of Data Science
2. Mathematical and Statistical Foundations
3. Computational Foundations and Tools
4. Data Acquisition and Management
5. Exploratory Data Analysis
6. Feature Engineering and Selection
7. Machine Learning Fundamentals
8. Advanced Machine Learning Topics
9. Big Data and Distributed Computing
10. Data Visualization and Communication
11. Model Deployment and MLOps
12. Ethics and Responsible AI
11.
Model Deployment and MLOps
11.1.
Model Deployment Strategies
11.1.1.
Batch Prediction
11.1.1.1.
Scheduled Jobs
11.1.1.2.
Data Pipeline Integration
11.1.1.3.
Performance Monitoring
11.1.2.
Real-time Prediction
11.1.2.1.
API Endpoints
11.1.2.2.
Streaming Predictions
11.1.2.3.
Latency Optimization
11.1.3.
Edge Deployment
11.1.3.1.
Mobile Devices
11.1.3.2.
IoT Devices
11.1.3.3.
Offline Capabilities
11.2.
Containerization
11.2.1.
Docker Fundamentals
11.2.1.1.
Images and Containers
11.2.1.2.
Dockerfile Creation
11.2.1.3.
Container Orchestration
11.2.2.
Kubernetes
11.2.2.1.
Pods and Services
11.2.2.2.
Deployments
11.2.2.3.
Scaling and Load Balancing
11.3.
Model Serving
11.3.1.
REST APIs
11.3.1.1.
Flask
11.3.1.2.
FastAPI
11.3.1.3.
Django REST Framework
11.3.2.
Model Serving Platforms
11.3.2.1.
TensorFlow Serving
11.3.2.2.
MLflow
11.3.2.3.
Seldon Core
11.3.2.4.
KubeFlow
11.3.3.
Cloud Deployment
11.3.3.1.
AWS SageMaker
11.3.3.2.
Google AI Platform
11.3.3.3.
Azure Machine Learning
11.4.
MLOps Practices
11.4.1.
Version Control for ML
11.4.1.1.
Model Versioning
11.4.1.2.
Data Versioning
11.4.1.3.
Experiment Tracking
11.4.2.
Continuous Integration/Continuous Deployment
11.4.2.1.
Automated Testing
11.4.2.2.
Model Validation
11.4.2.3.
Deployment Pipelines
11.4.3.
Monitoring and Observability
11.4.3.1.
Model Performance Monitoring
11.4.3.2.
Data Drift Detection
11.4.3.3.
System Health Monitoring
11.4.3.4.
Alerting Systems
11.4.4.
Model Governance
11.4.4.1.
Model Registry
11.4.4.2.
Approval Workflows
11.4.4.3.
Compliance Tracking
11.5.
A/B Testing for ML
11.5.1.
Experimental Design
11.5.1.1.
Hypothesis Formation
11.5.1.2.
Sample Size Calculation
11.5.1.3.
Randomization Strategies
11.5.2.
Implementation
11.5.2.1.
Traffic Splitting
11.5.2.2.
Feature Flags
11.5.2.3.
Gradual Rollouts
11.5.3.
Analysis
11.5.3.1.
Statistical Testing
11.5.3.2.
Business Metrics
11.5.3.3.
Long-term Effects
11.6.
Model Maintenance
11.6.1.
Model Retraining
11.6.1.1.
Trigger Conditions
11.6.1.2.
Automated Retraining
11.6.1.3.
Human-in-the-loop
11.6.2.
Performance Degradation
11.6.2.1.
Causes and Detection
11.6.2.2.
Mitigation Strategies
11.6.3.
Model Updates
11.6.3.1.
Backward Compatibility
11.6.3.2.
Rollback Strategies
11.6.3.3.
Change Management
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12. Ethics and Responsible AI