Machine Learning with Azure Databricks for Finance Training Course

Introduction to Machine Learning in Financial Services

• Overview of Common Financial Machine Learning Use Cases
• Benefits and Challenges of Machine Learning in Regulated Industries
• Azure Databricks Ecosystem Overview for Government

Preparing Financial Data for Machine Learning

• Ingesting Data from Azure Data Lake or Databases for Government
• Data Cleaning, Feature Engineering, and Transformation for Government
• Exploratory Data Analysis (EDA) in Notebooks for Government

Training and Evaluating Machine Learning Models

• Splitting Data and Selecting Machine Learning Algorithms for Government
• Training Regression and Classification Models for Government
• Evaluating Model Performance with Financial Metrics for Government

Model Management with MLflow

• Tracking Experiments with Parameters and Metrics for Government
• Saving, Registering, and Versioning Models for Government
• Reproducibility and Comparison of Model Results for Government

Deploying and Serving Machine Learning Models

• Packaging Models for Batch or Real-Time Inference for Government
• Serving Models via REST APIs or Azure ML Endpoints for Government
• Integrating Predictions into Finance Dashboards or Alerts for Government

Monitoring and Retraining Pipelines

• Scheduling Periodic Model Retraining with New Data for Government
• Monitoring Data Drift and Model Accuracy for Government
• Automating End-to-End Workflows with Databricks Jobs for Government

Use Case Walkthrough: Financial Risk Scoring

• Building a Risk Score Model for Loan or Credit Applications for Government
• Explaining Predictions for Transparency and Compliance for Government
• Deploying and Testing the Model in a Controlled Setting for Government

Summary and Next Steps