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Course Outline
Introduction to Machine Learning in Financial Services
- Survey of prevalent machine learning applications within the financial sector
- Advantages and constraints associated with implementing machine learning in regulated environments for government oversight and compliance
- Overview of the Azure Databricks technical environment
Preparation of Financial Data for Machine Learning Processes
- Acquisition of data from Azure Data Lake or relational databases
- Data cleansing, feature engineering, and transformation procedures
- Execution of exploratory data analysis within notebook interfaces
Training and Evaluation of Machine Learning Models
- Data partitioning and selection of appropriate machine learning algorithms
- Development of regression and classification models
- Assessment of model performance utilizing financial-specific metrics for government auditability
Management of Models via MLflow
- Documentation of experiments through parameter and metric tracking
- Procedures for saving, registering, and version-controlling models
- Ensuring reproducibility and facilitating comparison of model outcomes
Deployment and Serving of Machine Learning Models
- Packaging models for batch processing or real-time inference capabilities
- Delivery of models through REST APIs or Azure ML endpoints for government integration
- Integration of predictive outputs into financial dashboards and alert systems
Monitoring and Retraining Pipelines
- Scheduling periodic model retraining using updated datasets
- Surveillance of data drift and maintenance of model accuracy
- Automation of end-to-end workflows utilizing Databricks Jobs for operational efficiency
Case Study: Financial Risk Scoring Implementation
- Construction of a risk scoring model for loan or credit applications
- Explanation of predictions to ensure transparency and regulatory compliance for government standards
- Deployment and validation of the model within a controlled environment
Summary and Strategic Next Steps
Requirements
- Demonstrated proficiency in foundational machine learning principles
- Practical expertise with Python and data analytics methodologies
- Knowledge of financial data structures and reporting standards
Audience
- Data scientists and machine learning engineers within the financial sector
- Data analysts advancing into machine learning positions
- Technology specialists deploying predictive analytics for government and financial applications
7 Hours
