Machine Learning with Azure Databricks for Finance Training Course

Introduction to ML in Financial Services

• Survey of prevalent financial ML use cases
• Advantages and challenges of implementing ML in regulated industries
• Overview of the Azure Databricks ecosystem

Preparing Financial Data for ML

• Ingesting data from Azure Data Lake or existing databases
• Data cleansing, feature engineering, and transformation processes
• Conducting Exploratory Data Analysis (EDA) within notebooks

Training and Evaluating ML Models

• Data splitting strategies and selection of appropriate ML algorithms
• Training regression and classification models
• Assessing model performance using domain-specific financial metrics

Model Management with MLflow

• Tracking experiments by monitoring parameters and key metrics
• Storing, registering, and managing model versions
• Ensuring reproducibility and facilitating the comparison of model outcomes

Deploying and Serving ML Models

• Packaging models for either batch or real-time inference scenarios
• Serving models through REST APIs or Azure ML endpoints
• Embedding predictions into financial dashboards or alerting systems

Monitoring and Retraining Pipelines

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

Use Case Walkthrough: Financial Risk Scoring

• Developing a risk scoring model for loan or credit applications
• Interpreting predictions to support transparency and compliance requirements
• Deploying and testing the model within a controlled environment