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Course Outline
Introduction to Machine Learning in Financial Services
- Overview of prevalent ML use cases in finance
- Advantages and challenges of ML in regulated industries
- Overview of the Azure Databricks ecosystem
Preparing Financial Data for Machine Learning
- Ingesting data from Azure Data Lake or relational databases
- Data cleaning, feature engineering, and transformation techniques
- Performing exploratory data analysis (EDA) in notebooks
Training and Evaluating Machine Learning Models
- Data splitting strategies and selecting appropriate ML algorithms
- Training regression and classification models
- Assessing model performance using financial metrics
Model Management with MLflow
- Tracking experiments via parameters and metrics
- Saving, 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 via REST APIs or Azure ML endpoints
- Integrating predictions into financial dashboards or alert systems
Monitoring and Retraining Pipelines
- Scheduling periodic model retraining with fresh data
- Monitoring data drift and maintaining model accuracy
- Automating end-to-end workflows using Databricks Jobs
Use Case Walkthrough: Financial Risk Scoring
- Developing a risk score model for loan or credit applications
- Interpreting predictions to ensure transparency and compliance
- Deploying and testing the model in a controlled environment
Summary and Next Steps
Requirements
- A solid understanding of fundamental machine learning concepts
- Practical experience with Python and data analysis
- Familiarity with financial datasets or reporting structures
Target Audience
- Data scientists and ML engineers working within financial services
- Data analysts transitioning into machine learning roles
- Technology professionals deploying predictive solutions in the finance sector
7 Hours
