Machine Learning with Azure Databricks for Finance Training Course

Introduction to ML in Financial Services

• Overview of common machine learning use cases in finance
• Advantages and challenges of implementing ML in regulated industries
• Overview of the Azure Databricks ecosystem

Preparing Financial Data for ML

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

Training and Evaluating ML Models

• Data splitting strategies and selecting appropriate ML algorithms
• Training regression and classification models
• Evaluating model performance using specific financial metrics

Model Management with MLflow

• Tracking experiments along with parameters and metrics
• Saving, registering, and managing model versions
• Ensuring reproducibility and comparing model results

Deploying and Serving ML Models

• Packaging models for batch or real-time inference
• Serving models through REST APIs or Azure ML endpoints
• Integrating predictions into finance 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