UsefulLinks
Computer Science
Artificial Intelligence
Machine Learning
Machine Learning in Finance
1. Foundations of Machine Learning in Finance
2. Data Sourcing and Management
3. Algorithmic Trading and Strategy Development
4. Risk Management and Portfolio Optimization
5. Fraud Detection and Compliance
6. Advanced Machine Learning Applications
7. Model Development and Validation
8. Implementation and Production Systems
9. Regulatory Framework and Ethics
10. Emerging Trends and Future Directions
8.
Implementation and Production Systems
8.1.
MLOps for Finance
8.1.1.
Model Lifecycle Management
8.1.1.1.
Version Control
8.1.1.2.
Experiment Tracking
8.1.1.3.
Model Registry
8.1.2.
Deployment Strategies
8.1.2.1.
Batch Prediction
8.1.2.2.
Real-Time Inference
8.1.2.3.
A/B Testing
8.1.3.
Infrastructure Requirements
8.1.3.1.
Computing Resources
8.1.3.2.
Data Storage
8.1.3.3.
Network Latency
8.1.4.
Monitoring and Maintenance
8.1.4.1.
Model Performance Tracking
8.1.4.2.
Data Drift Detection
8.1.4.3.
Model Retraining
8.2.
Technology Stack
8.2.1.
Programming Languages
8.2.1.1.
Python Ecosystem
8.2.1.2.
R for Statistics
8.2.1.3.
C++ for Performance
8.2.2.
Machine Learning Frameworks
8.2.2.1.
Scikit-learn
8.2.2.2.
TensorFlow
8.2.2.3.
PyTorch
8.2.2.4.
XGBoost
8.2.3.
Data Processing Tools
8.2.3.1.
Pandas
8.2.3.2.
NumPy
8.2.3.3.
Apache Spark
8.2.3.4.
Dask
8.2.4.
Deployment Technologies
8.2.4.1.
Docker Containers
8.2.4.2.
Kubernetes
8.2.4.3.
Cloud Platforms
8.2.4.4.
API Development
8.3.
Data Management
8.3.1.
Data Architecture
8.3.1.1.
Data Lakes
8.3.1.2.
Data Warehouses
8.3.1.3.
Real-Time Streaming
8.3.2.
Data Quality Management
8.3.2.1.
Data Validation
8.3.2.2.
Data Lineage
8.3.2.3.
Data Governance
8.3.3.
Security and Privacy
8.3.3.1.
Data Encryption
8.3.3.2.
Access Controls
8.3.3.3.
Privacy Preservation
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9. Regulatory Framework and Ethics