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Duration 7 hours
Course Outline
Introduction to Machine Learning in Financial Services
- Survey of typical machine learning applications in finance
- Analysis of benefits and complexities of deploying ML in regulated sectors
- Overview of the Azure Databricks ecosystem
Preparing Financial Data for Machine Learning
- Data ingestion strategies from Azure Data Lake and database sources
- Techniques for data cleansing, feature engineering, and transformation
- Conducting exploratory data analysis (EDA) using notebooks
Training and Assessing ML Models
- Data partitioning strategies and selection of appropriate ML algorithms
- Developing regression and classification models
- Assessing model efficacy using finance-specific performance metrics
Model Management via MLflow
- Experiment tracking with detailed parameters and metrics
- Processes for saving, registering, and versioning models
- Ensuring reproducibility and comparing model outcomes
Deployment and Serving of ML Models
- Packaging models for both batch processing and real-time inference
- Serving models through REST APIs or Azure ML endpoints
- Incorporating model predictions into financial dashboards or alert systems
Monitoring and Retraining Pipelines
- Scheduling routine model retraining using updated data
- Tracking data drift and maintaining model accuracy
- Automating comprehensive workflows utilizing Databricks Jobs
Case Study: Financial Risk Scoring
- Developing a risk scoring model for loan or credit applications
- Interpreting predictions to ensure transparency and regulatory compliance
- Deploying and validating the model in a controlled environment
Requirements
- A solid grasp of fundamental machine learning principles.
- Proficiency in Python and general data analysis techniques.
- Working knowledge of financial datasets or standard reporting practices.
Target Audience
- Data scientists and machine learning engineers operating within financial services.
- Data analysts aiming to transition into machine learning roles.
- Technology professionals responsible for implementing predictive solutions in the finance industry.
