Machine Learning with Azure Databricks for Finance Training Course

Introduction to ML in Financial Services

• Survey of typical machine learning applications in finance
• Advantages and hurdles of implementing ML in regulated industries
• Overview of the Azure Databricks ecosystem

Preparing Financial Data for ML

• Importing data from Azure Data Lake or database sources
• Performing data cleaning, feature engineering, and transformations
• Conducting exploratory data analysis (EDA) within notebooks

Training and Evaluating ML Models

• Partitioning data and choosing appropriate ML algorithms
• Training regression and classification models
• Assessing model performance using finance-specific metrics

Model Management with MLflow

• Tracking experiments through parameters and metrics
• Storing, registering, and managing model versions
• Ensuring reproducibility and comparing model outcomes

Deploying and Serving ML Models

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

Monitoring and Retraining Pipelines

• Scheduling regular model retraining with updated data
• Monitoring for data drift and maintaining model accuracy
• Automating end-to-end workflows using Databricks Jobs

Use Case Walkthrough: Financial Risk Scoring

• Constructing a risk scoring model for loan or credit applications
• Interpreting predictions to ensure transparency and regulatory compliance
• Deploying and validating the model in a controlled environment