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 complexities of implementing ML in regulated sectors
• Overview of the Azure Databricks ecosystem

Preparing Financial Data for ML

• Acquiring data from Azure Data Lake or various databases
• Data cleansing, feature engineering, and transformation processes
• Conducting exploratory data analysis (EDA) using notebooks

Training and Evaluating ML Models

• Data partitioning strategies and selection of appropriate ML algorithms
• Training regression and classification models
• Assessing model effectiveness using domain-specific financial metrics

Model Management with MLflow

• Experiment tracking with associated parameters and metrics
• Storing, registering, and versioning models
• Ensuring reproducibility and comparing model outcomes

Deploying and Serving ML Models

• Packaging models for batch processing or real-time inference
• Serving models through 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 tracking model accuracy
• Automating end-to-end workflows using Databricks Jobs

Case Study: Financial Risk Scoring

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