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Course Outline
Introduction to ML in Financial Services
- Overview of common financial ML use cases.
- Benefits and challenges of ML in regulated industries.
- Overview of the Azure Databricks ecosystem.
Preparing Financial Data for ML
- Ingesting data from Azure Data Lake or databases.
- Data cleaning, feature engineering, and transformation.
- Exploratory data analysis (EDA) in notebooks.
Training and Evaluating ML Models
- Splitting data and selecting ML algorithms.
- Training regression and classification models.
- Evaluating model performance using financial metrics.
Model Management with MLflow
- Tracking experiments with parameters and metrics.
- Saving, registering, and versioning models.
- Ensuring reproducibility and comparison of model results.
Deploying and Serving ML Models
- Packaging models for batch or real-time inference.
- Serving models via REST APIs or Azure ML endpoints.
- Integrating predictions into finance dashboards or alerts.
Monitoring and Retraining Pipelines
- Scheduling periodic model retraining with new data.
- Monitoring data drift and model accuracy.
- Automating end-to-end workflows with Databricks Jobs.
Use Case Walkthrough: Financial Risk Scoring
- Building a risk score model for loan or credit applications.
- Explaining predictions for transparency and compliance.
- Deploying and testing the model in a controlled setting.
Summary and Next Steps
Requirements
- A foundational understanding of machine learning concepts.
- Experience with Python and data analysis.
- Familiarity with financial datasets or reporting.
Audience
- Data scientists and ML engineers working in financial services.
- Data analysts transitioning into ML roles.
- Technology professionals implementing predictive solutions in finance.
7 Hours
