Machine Learning with Azure Databricks for Finance

• 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

Azure Databricks is a cloud-based collaborative platform that supports big data analytics and machine learning using Apache Spark and the Microsoft Azure ecosystem.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data professionals who wish to design, build, and deploy machine learning models using Azure Databricks for financial use cases.

By the end of this training, participants will be able to:

• Build and train machine learning models using financial data in Azure Databricks.
• Use MLflow to track experiments, manage models, and deploy solutions.
• Apply models to detect anomalies, assess risks, or forecast trends in finance.
• Integrate ML models into scalable pipelines for continuous evaluation.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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