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Machine Learning in Production with Python

Michelle Conway, Lead Data Scientist at Lloyds Banking Group, will walk you through a simple machine learning example on banking data, including feature engineering, training a model, making predictions, and assessing model performance.
Michelle Conway
Michelle Conway

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Increasingly, a machine learning project is only complete once your code goes into production as part of a data product. To achieve this, you need to think about how to write code that will be robust and performant. These software development techniques are becoming essential skills for machine learning engineers.

In this session, Michelle Conway, Lead Data Scientist at Lloyds Banking Group, will walk you through a simple machine learning example on banking data, including feature engineering, training a model, making predictions, and assessing model performance. Next, you'll see how to adjust the code to make it suitable for use in production.

Key Takeaways:

  • Learn about machine learning workflows in Python.
  • Learn about the challenges of putting machine learning code into production.
  • Learn how to engineer your machine learning code for production.

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Data ScienceMachine LearningPythonData Scientist

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