Machine Learning with Azure Databricks for Finance Training Course

Introduction to Machine Learning in Financial Services

• A review of prevalent machine learning applications in finance
• The advantages and obstacles of using ML in heavily regulated industries
• An overview of the Azure Databricks ecosystem

Preparing Financial Data for Machine Learning

• Importing data from Azure Data Lake or database systems
• Data cleansing, feature engineering, and transformation processes
• Conducting exploratory data analysis (EDA) using notebooks

Training and Assessing Machine Learning Models

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

Managing Models with MLflow

• Monitoring experiments through parameters and metrics
• Storing, registering, and managing model versions
• Ensuring reproducibility and comparing model outcomes

Deploying and Serving Machine Learning Models

• Preparing models for batch processing or real-time inference
• Serving models through REST APIs or Azure ML endpoints
• Embedding 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

Practical Case Study: Financial Risk Scoring

• Creating a risk score model for loan or credit applications
• Explaining predictions to ensure transparency and compliance
• Deploying and testing the model within a controlled environment