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Machine Learning in Production with Python

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.
Tuesday, July 02 11AM ET
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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.

Presenter Bio

Michelle Conway Headshot
Michelle ConwayLead Data Scientist at Lloyds Banking Group

Michelle is a full-stack data scientist with experience in retail, insurance, finance, telecommunications, and IT consulting. As a senior manager at Lloyds, she leads a high-performance MLOps team creating data products. Michelle is also an ambassador for Women in Data and was on the Women in Data 'Twenty in Data & Tech 2023' list.

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