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Developing Machine Learning Models for Production

Sinan Ozdemir

Data Scientist, Entrepreneur, and Author

Move from experiment to production

• Identify what makes an ML experiment ready to be moved into production
• Recognize the causes of technical debt

• Write effective documentation and maintainable code
• Effective documentation and maintainable code lead to reduced technical debt

Ensure reproducibility

• Ensure reproducibility in machine learning
• Perform feature engineering to optimize machine learning pipeline performance

• Design reproducible experiments
• Implement data and model versioning to track changes

Deploying ML models

• Package ML models and environments for deployment in different frameworks
• Ensure scalability of machine learning models to handle larger workloads

• Four MLOps principles: Continuous Integration, Continuous Delivery, Continuous Training, and Continuous Monitoring

Test + Evaluate

• Different types of testing routines - unit tests, integration tests, smoke tests

• Evaluate model reliability to ensure that the model produces accurate results
• Test models to identify potential issues and improve performance

Keep going!

• Apply what you have learned in this course to a real-world project
• Ensure reproducibility, scalability, reliability, and monitor for data drift
• Set up an experimentation framework for A/B testing your model
• Regularly re-train and update your model to ensure it remains relevant and accurate over time
• Keep learning and exploring new developments in the field of machine learning
• Continue to improve your skills and expertise

Let's go!

Developing Machine Learning Models for Production