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Course Outline
Introduction to Machine Learning in Financial Services
- Overview of common machine learning use cases in finance
- Benefits and challenges of implementing machine learning in regulated industries
- Overview of the Azure Databricks ecosystem
Preparing Financial Data for Machine Learning
- Ingesting data from Azure Data Lake or databases
- Data cleaning, feature engineering, and transformation
- Exploratory data analysis (EDA) using notebooks
Training and Evaluating Machine Learning Models
- Data splitting and selection of machine learning 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 comparing model results
Deploying and Serving Machine Learning Models
- Packaging models for batch or real-time inference
- Serving models via REST APIs or Azure ML endpoints
- Integrating predictions into finance dashboards or alert systems
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
- Proficiency in Python and data analysis
- Familiarity with financial datasets or reporting standards
Target Audience
- Data scientists and machine learning engineers within the financial services sector
- Data analysts moving into machine learning roles
- Technology professionals deploying predictive solutions in finance
7 Hours
