Machine Learning with Azure Databricks for Finance Training Course

Intro to Machine Learning in Financial Services

• Survey of typical ML applications in finance
• Advantages and hurdles of adopting ML in highly regulated industries
• Overview of the Azure Databricks ecosystem

Preparing Financial Data for Machine Learning

• Data ingestion from Azure Data Lake or database sources
• Techniques for data cleaning, feature engineering, and transformation
• Conducting exploratory data analysis (EDA) using notebooks

Training and Assessing ML Models

• Data partitioning and algorithm selection strategies
• Building regression and classification models
• Evaluating model efficacy using finance-specific metrics

Managing Models via MLflow

• Recording experiments with associated parameters and performance metrics
• Storing, registering, and versioning model artifacts
• Ensuring reproducibility and comparing model outcomes

Deployment and Serving of ML Models

• Preparing 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 Workflows

• Automating periodic model retraining with fresh data
• Tracking data drift and monitoring model accuracy
• Automating end-to-end processes using Databricks Jobs

Case Study: Financial Risk Assessment

• Constructing a risk scoring model for loan or credit applications
• Interpreting predictions to ensure transparency and regulatory compliance
• Deploying and validating the model in a controlled environment