Machine Learning with Azure Databricks for Finance Training Course

Introduction to ML in Financial Services

• Overview of prevalent machine learning use cases in finance.
• Advantages and challenges of implementing ML in regulated industries.
• Overview of the Azure Databricks ecosystem.

Preparing Financial Data for ML

• Ingesting data 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 splitting strategies and selection of appropriate ML algorithms.
• Training regression and classification models.
• Assessing model performance using finance-specific metrics.

Model Management with MLflow

• Tracking experiments via 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 through 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 for data drift and maintaining 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.
• Explaining predictions to ensure transparency and compliance.
• Deploying and testing the model in a controlled environment.