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Duration 7 hours
Course Outline
Introduction to Machine Learning Applications in Financial Services
- Survey of prevalent machine learning applications within the financial sector
- Advantages and operational hurdles associated with adopting machine learning in highly regulated environments
- Examination of the Azure Databricks ecosystem components
Preparation of Financial Data for Machine Learning
- Acquisition of data from Azure Data Lake or database repositories
- Data cleansing, feature construction, and structural transformation
- Conducting exploratory data analysis (EDA) within notebook environments
Training and Assessment of Machine Learning Models
- Partitioning datasets and selection of appropriate machine learning algorithms
- Development of regression and classification models
- Evaluation of model efficacy using sector-specific financial metrics
Model Governance through MLflow
- Documentation of experimental runs, including parameters and performance indicators
- Storage, registration, and version control of model artifacts
- Ensuring reproducibility and comparative analysis of model outcomes
Deployment and Service Delivery of Machine Learning Models
- Preparation of models for batch processing or real-time inference scenarios
- Delivery of models via RESTful APIs or Azure Machine Learning endpoints
- Integration of predictive outputs into financial dashboards or alert systems
Surveillance and Retraining Automation
- Scheduling recurrent model retraining cycles with updated datasets
- Monitoring for data drift and maintaining model accuracy standards
- Automation of comprehensive workflows utilizing Databricks Jobs
Case Study Implementation: Financial Risk Scoring
- Construction of a risk scoring model for loan or credit application processing
- Interpretation of predictions to ensure transparency and regulatory compliance
- Deployment and validation of the model in a controlled environment
Conclusion and Strategic Recommendations
Requirements
- Foundational knowledge of basic machine learning principles
- Proficiency in Python and data analysis techniques
- Familiarity with financial datasets or reporting structures
Intended Audience
- Data scientists and machine learning engineers within the financial services sector
- Data analysts advancing into machine learning roles
- Technology professionals implementing predictive solutions for financial governance for government entities or private institutions
