Machine Learning with Azure Databricks for Finance Training Course

Introduction to Machine Learning in Financial Services

• An overview of common ML use cases in finance
• Advantages and challenges of applying ML in regulated industries
• An introduction to the Azure Databricks ecosystem

Preparng Financial Data for Machine Learning

• Ingesting data from Azure Data Lake or other database sources
• Data cleaning, feature engineering, and transformation processes
• Conducting exploratory data analysis (EDA) using notebooks

Training and Evaluating ML Models

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

Model Management via MLflow

• Tracking experiments through parameters and metrics
• Saving, registering, and versioning models
• Ensuring reproducibility and comparing model results

Deploying and Serving ML Models

• Packaging models for batch processing or real-time inference
• Serving models through REST APIs or Azure ML endpoints
• Integrating predictions into financial dashboards or alert systems

Monitoring and Retraining Pipelines

• Scheduling periodic model retraining with updated data
• Monitoring data drift and maintaining model accuracy
• Automating end-to-end workflows using 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