Machine Learning with Azure Databricks for Finance Training Course

Introduction to ML in Financial Services

• An overview of prevalent machine learning use cases in finance.
• The advantages and challenges of implementing ML in regulated industries.
• An introduction to the Azure Databricks ecosystem.

Preparing Financial Data for ML

• Ingesting data from Azure Data Lake or traditional databases.
• Data cleansing, feature engineering, and transformation processes.
• Conducting exploratory data analysis (EDA) within notebooks.

Training and Evaluating ML Models

• Splitting datasets and selecting appropriate machine learning algorithms.
• Training regression and classification models.
• Evaluating model performance using finance-specific metrics.

Model Management with MLflow

• Tracking experiments through parameters and metrics.
• Saving, 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 via REST APIs or Azure ML endpoints.
• Integrating predictions into financial 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.