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 complexities of adopting ML in regulated industries
• Overview of the Azure Databricks ecosystem

Preparing Financial Data for ML

• Retrieving data from Azure Data Lake or database sources
• Data cleansing, feature engineering, and transformation techniques
• Conducting exploratory data analysis (EDA) using notebooks

Training and Evaluating ML Models

• Data partitioning and the selection of appropriate ML algorithms
• Developing regression and classification models
• Assessing model performance using finance-specific metrics

Model Management with MLflow

• Tracking experiments by monitoring parameters and metrics
• Storing, registering, and versioning models effectively
• 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
• Incorporating predictions into financial dashboards or alert systems

Monitoring and Retraining Pipelines

• Scheduling regular model retraining with updated data
• Monitoring for data drift and maintaining model accuracy
• Automating end-to-end workflows using Databricks Jobs

Case Study: Financial Risk Scoring

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