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Course Outline
Introduction to Machine Learning in Financial Services
- Key financial use cases for machine learning.
- Advantages and complexities of ML in regulated industries.
- 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 processes.
- Conducting exploratory data analysis (EDA) using notebooks.
Training and Evaluating Machine Learning Models
- Data partitioning and algorithm selection strategies.
- Developing regression and classification models.
- Assessing model performance using domain-specific financial metrics.
Managing Models with MLflow
- Experiment tracking through parameters and performance metrics.
- Model storage, registration, and versioning practices.
- Ensuring reproducibility and comparing model outcomes.
Deployment and Serving of Machine Learning Models
- Packaging models for batch processing or real-time inference.
- Exposing models via REST APIs or Azure ML endpoints.
- Integrating 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.
Practical Walkthrough: Financial Risk Scoring
- Developing a risk score model for loan or credit applications.
- Interpreting predictions to ensure transparency and regulatory compliance.
- Testing and deploying the model within a controlled environment.
Requirements
- A solid grasp of fundamental machine learning principles.
- Proficiency in Python and data analysis techniques.
- Knowledge of financial data structures or reporting standards.
Target Audience
- Data scientists and ML engineers operating within financial services.
- Data analysts advancing their careers into machine learning roles.
- Technology specialists implementing predictive analytics in the finance industry.
7 Hours
