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Duration 7 hours
Course Outline
Introduction to ML in the Financial Sector
- Survey of prevalent machine learning applications in finance
- Advantages and obstacles of implementing ML in regulated industries
- Overview of the Azure Databricks ecosystem
Preparing Financial Data for Machine Learning
- Importing data from Azure Data Lake or other database sources
- Performing data cleaning, feature engineering, and transformations
- Conducting exploratory data analysis (EDA) within notebooks
Training and Assessing ML Models
- Data partitioning and selection of appropriate ML algorithms
- Developing regression and classification models
- Measuring model effectiveness using financial-specific metrics
Managing Models with MLflow
- Monitoring experiments by tracking parameters and performance metrics
- Storing, registering, and versioning models
- Ensuring reproducibility and comparing model outcomes
Deployment and Serving of ML Models
- Preparing models for batch processing or real-time inference
- Delivering models through REST APIs or Azure ML endpoints
- Incorporating predictions into financial dashboards or alerting systems
Monitoring and Retraining Workflows
- Scheduling regular model retraining with updated data
- Tracking data drift and maintaining model accuracy
- Automating comprehensive workflows using Databricks Jobs
Case Study: Financial Risk Scoring
- Creating a risk scoring model for loan or credit assessments
- Interpreting predictions to ensure transparency and regulatory compliance
- Implementing and testing the model within a controlled environment
Requirements
- A solid grasp of fundamental machine learning principles
- Proficiency in Python and data analysis techniques
- Working knowledge of financial datasets or reporting standards
Intended Audience
- Data scientists and machine learning engineers operating within financial services
- Data analysts aiming to transition into machine learning roles
- Technology professionals tasked with implementing predictive analytics in finance
