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Duration 7 hours
Course Outline
Introduction to Machine Learning in Financial Services
- Overview of typical ML applications in finance
- Advantages and challenges of adopting ML in regulated industries
- Brief overview of the Azure Databricks ecosystem
Preparation of Financial Data for Machine Learning
- Data ingestion from Azure Data Lake or standard databases
- Techniques for data cleaning, feature engineering, and transformation
- Conducting Exploratory Data Analysis (EDA) within notebooks
Training and Evaluation of ML Models
- Data splitting strategies and algorithm selection
- Building regression and classification models
- Assessing model performance using finance-specific metrics
Model Management via MLflow
- Experiment tracking with detailed parameters and metrics
- Model storage, registration, and version control
- Ensuring reproducibility and comparing model outcomes
Deployment and Serving of ML Models
- Packaging models for batch processing or real-time inference
- Serving models through REST APIs or Azure ML endpoints
- Embedding 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
- Developing a risk scoring model for loan or credit applications
- Providing explanations for predictions to ensure transparency and compliance
- Deploying and testing the model in a controlled environment
Requirements
- A solid grasp of fundamental machine learning concepts.
- Proficiency in Python and data analysis techniques.
- A working familiarity with financial datasets or reporting standards.
Intended Audience
- Data scientists and ML engineers working within financial services.
- Data analysts aiming to transition into machine learning roles.
- Technical professionals responsible for implementing predictive solutions in the finance industry.
