Machine Learning with Azure Databricks for Finance Training Course

Introduction to Machine Learning in Financial Services

• Survey of prevalent ML applications in finance
• Advantages and complexities of ML within regulated industries
• Overview of the Azure Databricks ecosystem

Preparing Financial Data for Machine Learning

• Data ingestion from Azure Data Lake or database sources
• Data cleansing, feature engineering, and transformation techniques
• Performing Exploratory Data Analysis (EDA) in notebooks

Training and Evaluating Machine Learning Models

• Data partitioning and algorithm selection strategies
• Training regression and classification models
• Assessing model performance using finance-specific metrics

Managing Models with MLflow

• Tracking experiments via parameters and metrics
• Model persistence, registration, and version control
• Ensuring reproducibility and comparing model outcomes

Deploying and Serving Machine Learning 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 regular model retraining with updated data
• Monitoring for data drift and tracking model accuracy
• Automating end-to-end workflows using Databricks Jobs

Use Case Walkthrough: Financial Risk Scoring

• Constructing a risk score model for loan or credit applications
• Interpreting predictions for transparency and regulatory compliance
• Deploying and testing the model in a controlled environment