Machine Learning with Azure Databricks for Finance Training Course

Introduction to Machine Learning in Financial Services

• Summary of typical financial machine learning applications
• Advantages and challenges of machine learning in regulated sectors
• Overview of the Azure Databricks ecosystem

Preparing Financial Data for Machine Learning

• Acquiring data from Azure Data Lake or database sources
• Data cleansing, feature creation, and transformation processes
• Conducting exploratory data analysis (EDA) using notebooks

Training and Assessing Machine Learning Models

• Data partitioning and selection of appropriate algorithms
• Training regression and classification models
• Evaluating model efficacy using financial-specific metrics

Managing Models with MLflow

• Monitoring experiments through parameters and metrics
• Saving, registering, and versioning models
• Ensuring reproducibility and comparing model outcomes

Deployment and Serving of Machine Learning 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 regular model retraining with updated data
• Tracking data drift and maintaining model accuracy
• Automating end-to-end workflows using Databricks Jobs

Case Study: Financial Risk Scoring

• Creating a risk score model for loan or credit assessments
• Interpreting predictions to ensure transparency and compliance
• Deploying and validating the model in a secure environment