Machine Learning with Azure Databricks for Finance Training Course

Introduction to ML in Financial Services

• An overview of prevalent financial ML use cases
• The advantages and complexities of implementing ML in regulated industries
• An introduction to the Azure Databricks ecosystem

Preparing Financial Data for ML

• Importing data from Azure Data Lake or traditional databases
• Techniques for data cleaning, feature engineering, and transformation
• Conducting Exploratory Data Analysis (EDA) using notebooks

Training and Evaluating ML Models

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

Model Management with MLflow

• Monitoring experiments through parameters and performance metrics
• Processes for saving, registering, and versioning models
• Ensuring reproducibility and comparing model outcomes

Deploying and Serving ML Models

• Preparing models for batch processing or real-time inference
• Delivering models via REST APIs or Azure ML endpoints
• Incorporating predictions into financial dashboards or alert systems

Monitoring and Retraining Pipelines

• Scheduling routine model retraining using updated data
• Tracking data drift and maintaining model accuracy
• Automating end-to-end workflows utilizing Databricks Jobs

Case Study: Financial Risk Scoring

• Constructing a risk score model for loan or credit applications
• Interpreting predictions to ensure transparency and compliance
• Implementing and testing the model in a controlled environment