Machine Learning and AI Foundations: Predictive Modeling Strategy at Scale

Introduction

• Scaling machine learning initiatives
• Defining terms

1. The Phases of a Machine Learning Project

• Data and supervised machine learning
• The nine big data bottlenecks
• The stages of predictive analytics data
• Why you might have too little data

2. Designing a Machine Learning Dataset

• How much data do I need?
• Balancing
• Who truly has big data?
• Assessing data
• Selecting: Data that should be left out
• Seasonality and time alignment

3. Data Prep Challenges

• Data and the data scientist
• Aggregate and restructure
• Dummy coding
• Feature engineering

4. Modeling Challenges

• Understanding the modeling process
• Slow algorithms: Brute force
• Slow algorithms: More calculations
• Slow algorithms: More models
• How to sample properly
• Modeling with missing data

5. Scoring

• Scoring traditional ML models
• Scoring a black box model
• Scoring an ensemble

6. Deployment

• Batch vs. real-time scoring
• Data prep and scoring
• Combining batch and real-time scoring

7. Monitoring and Maintenance

• What is model monitoring?
• How often should you rebuild?

Conclusion

• Next steps