Machine Learning with Azure Databricks for Finance Training Course

Introduction to ML in Financial Services

• Surveying common machine learning applications in finance.
• Discussing the advantages and hurdles of ML in regulated sectors.
• An overview of the Azure Databricks ecosystem.

Preparing Financial Data for ML

• Acquiring data from Azure Data Lake or relational databases.
• Data cleansing, feature engineering, and transformation techniques.
• Conducting exploratory data analysis (EDA) within notebooks.

Training and Evaluating ML Models

• Partitioning data and selecting appropriate ML algorithms.
• Training regression and classification models.
• Assessing model efficacy using relevant financial metrics.

Model Management with MLflow

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

Deploying and Serving ML Models

• Packaging models for 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 regular model retraining with updated data.
• Tracking data drift and maintaining model accuracy.
• Automating end-to-end workflows using Databricks Jobs.

Use Case Walkthrough: Financial Risk Scoring

• Constructing a risk score model for loan or credit applications.
• Interpreting predictions to ensure transparency and compliance.
• Deploying and validating the model in a controlled setting.