Machine Learning with Azure Databricks for Finance Training Course

Introduction to ML in Financial Services

• Survey of prevalent machine learning applications in finance
• Advantages and obstacles of implementing ML in regulated sectors
• Overview of the Azure Databricks ecosystem

Preparing Financial Data for ML

• Data ingestion from Azure Data Lake or database sources
• Data cleansing, feature engineering, and transformation processes
• Conducting exploratory data analysis (EDA) within notebooks

Training and Evaluating ML Models

• Data partitioning and selection of suitable ML algorithms
• Training regression and classification models
• Assessing model efficacy using finance-specific metrics

Model Management with MLflow

• Experiment tracking utilizing parameters and performance metrics
• Model saving, registration, and version control
• Ensuring reproducibility and comparing model outcomes

Deploying and Serving ML Models

• Model packaging for batch or real-time inference
• Model serving via REST APIs or Azure ML endpoints
• Integrating predictions into financial dashboards or alert systems

Monitoring and Retraining Pipelines

• Scheduling automated model retraining with updated data
• Monitoring data drift and model precision
• Automating end-to-end workflows using Databricks Jobs

Use Case Walkthrough: Financial Risk Scoring

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