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Duration 7 hours
Course Outline
Introduction to Machine Learning in Financial Services
- Survey of typical financial machine learning applications.
- Advantages and obstacles of employing ML in regulated sectors.
- Overview of the Azure Databricks ecosystem.
Preparing Financial Data for Machine Learning
- Data ingestion from Azure Data Lake or traditional databases.
- Processes for data cleaning, feature engineering, and transformation.
- Conducting exploratory data analysis (EDA) within notebooks.
Training and Assessing Machine Learning Models
- Data splitting strategies and algorithm selection.
- Training regression and classification models.
- Evaluating model performance using specific financial metrics.
Model Management via MLflow
- Tracking experiments by recording parameters and metrics.
- Storing, registering, and versioning models.
- Ensuring reproducibility and comparing model outcomes.
Deployment and Serving of Machine Learning Models
- Packaging models for either batch processing or real-time inference.
- Serving models through REST APIs or Azure ML endpoints.
- Incorporating predictions into financial dashboards or alert systems.
Monitoring and Retraining Pipelines
- Scheduling regular model retraining with updated data.
- Monitoring data drift and maintaining model accuracy.
- Automating end-to-end workflows using Databricks Jobs.
Case Study: Financial Risk Scoring
- Constructing a risk score model for loan or credit assessments.
- Interpreting predictions to ensure transparency and regulatory compliance.
- Deploying and testing the model in a controlled environment.
Requirements
- A foundational grasp of core machine learning principles.
- Proficiency in Python and data analysis techniques.
- Knowledge of financial datasets or reporting standards.
Target Audience
- Data scientists and ML engineers working within financial services.
- Data analysts seeking to transition into machine learning roles.
- Technology specialists implementing predictive solutions in the finance industry.
