Machine Learning with Azure Databricks for Finance Training Course

Introduction to ML in the Financial Sector

• An overview of prevalent machine learning applications in finance
• Exploring the advantages and complexities of ML in regulated industries
• A comprehensive look at the Azure Databricks ecosystem

Preparing Financial Data for ML

• Accessing data streams from Azure Data Lake or traditional databases
• Processes for data cleansing, feature engineering, and transformation
• Conducting Exploratory Data Analysis (EDA) within notebooks

Training and Assessing ML Models

• Techniques for data splitting and algorithm selection
• Training processes for regression and classification models
• Assessing model efficacy using domain-specific financial metrics

Model Management via MLflow

• Monitoring experiments through parameter and metric tracking
• Procedures for saving, registering, and versioning models
• Ensuring reproducibility and comparing model outcomes

Deployment and Serving ML Models

• Packaging models for either 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 automated model retraining with incoming data
• Tracking 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 assessments
• Interpreting predictions to ensure transparency and compliance
• Deploying and testing the model within a controlled environment