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Course Outline
Introduction to ML in Financial Services
- Overview of common financial ML use cases
- Benefits and challenges of ML in regulated industries
- Azure Databricks ecosystem overview
Preparing Financial Data for ML
- Ingesting data from Azure Data Lake or databases
- Data cleaning, feature engineering, and transformation
- Exploratory data analysis (EDA) in notebooks
Training and Evaluating ML Models
- Splitting data and selecting ML 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 ML 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
- An understanding of basic machine learning concepts
- Experience with Python and data analysis
- Familiarity with financial datasets or reporting
Audience
- Data scientists and ML engineers in financial services
- Data analysts transitioning to ML roles
- Technology professionals implementing predictive solutions in finance
7 Hours
Testimonials (1)
All the topics it covers, although many were very quick, give us an idea of what we will need to delve into. I also liked that we were able to do internships, although I insist, I think the course deserves more.
Sandra Mariela Lopez Bernal - Kueski
Course - Databricks
Machine Translated
