Machine Learning with Azure Databricks for Finance Training Course

Introduction to Machine Learning in Financial Services

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

Preparing Financial Data for Machine Learning

• Ingesting data from Azure Data Lake or existing databases
• Processes for data cleaning, feature engineering, and transformation
• Conducting exploratory data analysis (EDA) using notebooks

Training and Assessing ML Models

• Strategies for data splitting and algorithm selection
• Training regression and classification models
• Evaluating model performance using specific financial metrics

Managing Models with MLflow

• Tracking experiments through parameters and metrics
• Techniques for 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 for data drift and tracking model accuracy
• Automating end-to-end workflows using Databricks Jobs

Case Study: Financial Risk Scoring

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