Machine Learning with Azure Databricks for Finance Training Course

Introduction to ML in Financial Services

• Overview of prevalent financial ML use cases
• Advantages 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 database sources
• Data cleaning, feature engineering, and transformation processes
• Conducting exploratory data analysis (EDA) in notebooks

Training and Evaluating ML Models

• Data splitting strategies and ML algorithm selection
• Training regression and classification models
• Assessing model performance using financial metrics

Model Management with MLflow

• Tracking experiments via parameters and metrics
• Saving, registering, and versioning models
• Ensuring reproducibility and comparing 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 financial dashboards or alert systems

Monitoring and Retraining Pipelines

• Scheduling periodic model retraining with fresh 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 to ensure transparency and compliance
• Deploying and testing the model in a controlled environment