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Course Outline
Introduction to Machine Learning in Financial Services
- Overview of common financial machine learning use cases
- Benefits and challenges of machine learning in regulated industries for government
- Azure Databricks ecosystem overview
Preparing Financial Data for Machine Learning
- Ingesting data from Azure Data Lake or databases
- Data cleaning, feature engineering, and transformation
- Exploratory data analysis (EDA) in notebooks
Training and Evaluating Machine Learning Models
- Splitting data and selecting machine learning algorithms
- Training regression and classification models
- Evaluating model performance with financial metrics
Model Management with MLflow
- Tracking experiments with parameters and metrics
- Saving, registering, and versioning models
- Reproducibility and comparison of model results
Deploying and Serving Machine Learning Models
- Packaging models for batch or real-time inference
- Serving models via REST APIs or Azure ML endpoints
- Integrating predictions into finance dashboards or alerts
Monitoring and Retraining Pipelines
- Scheduling periodic model retraining with new data
- Monitoring data drift and model accuracy
- Automating end-to-end workflows with Databricks Jobs
Use Case Walkthrough: Financial Risk Scoring
- Building a risk score model for loan or credit applications
- Explaining predictions for transparency and compliance
- Deploying and testing the model in a controlled setting
Summary and Next Steps
Requirements
- A foundational understanding of machine learning concepts
- Practical experience with Python and data analysis
- Knowledge of financial datasets or reporting practices
Audience
- Data scientists and machine learning engineers in the financial services sector
- Data analysts looking to transition into machine learning roles
- Technology professionals implementing predictive solutions for government and finance
7 Hours
