Machine Learning with Azure Databricks for Finance Training Course

Introduction to Machine Learning in Financial Services

• Survey of prevalent machine learning applications in finance
• Advantages and complexities of machine learning in regulated sectors
• Overview of the Azure Databricks ecosystem

Preparing Financial Data for Machine Learning

• Data ingestion from Azure Data Lake or database sources
• Data cleansing, feature engineering, and transformation techniques
• Performing exploratory data analysis (EDA) within notebooks

Training and Evaluating Machine Learning Models

• Data partitioning and selection of appropriate machine learning algorithms
• Training regression and classification models
• Assessing model efficacy using financial performance metrics

Model Management via MLflow

• Monitoring experiments by tracking parameters and key metrics
• Storing, registering, and versioning models
• Ensuring reproducibility and comparing model outcomes

Deploying and Serving Machine Learning Models

• Packaging models for batch processing or real-time inference
• Serving models through REST APIs or Azure ML endpoints
• Incorporating predictions into financial dashboards or alert systems

Monitoring and Retraining Pipelines

• Automating periodic model retraining with fresh data
• Tracking data drift and maintaining model accuracy
• Streamlining end-to-end workflows using Databricks Jobs

Case Study: Financial Risk Scoring

• Constructing a risk scoring model for loan or credit assessments
• Explaining predictions to ensure transparency and regulatory compliance
• Deploying and validating the model in a secure environment