Data Science in Production with Python

Learn how to package your model from a Python notebook to a production environment.


What you will learn

Data Science in Production with Python

You have developed a model in your notebook, what’s next? You now need to package your work into a robust and maintainable Python project which you can monitor and interpret. This intensive course will help you achieve that. You will learn how to develop an end to end pipeline that brings your model to a production environment. You will learn the latest scikit-learn features to increase the robustness of your code as well as software and system engineering skills to increase your productivity and long-term maintenance of the models you build.

Latest scikit-learn tools and best practices to increase your productivity and maintainability of your models
Package your model from a Python notebook to a production environment
Best practices for testing, code quality and software engineering for Data Science
How to interpret a deployed model using techniques such as LIME, SHAP and ELI5
How to monitor the activity of your machine learning model


  • Have attended our 3-days core data science course or comfortable using Pandas and Scikit-learn.
  • Comfortable with fundamentals of Python programming


From Python Notebook to Packaging


Productionising your model


Monitoring & Debugging

Get in Touch


We will email you within the next 24 hours to arrange a quick call to help with any questions about the programme and recommend pre-course materials.

We look forward to speaking with you.

Dr. Raoul-Gabriel Urma

Dr. Raoul-Gabriel Urma