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Production Ready Machine Learning
Learn the best practice to bring your machine learning models into production with this practical training about how software engineers manage their code, how more advanced Python features can make your life easier, and how to go from notebooks to packages in this 2-day course.
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What you'll learn
You will learn:
During this training, you will go in-depth with the tools and technique to bring your Machine Learning code to production more easily. After this training, you are knowledgeable about the following topics.
- How documentation, testing, and modularity help creating more robust code
- How type hinting can greatly reduce the number of bugs in your code
- How continiuos delivery for Machine Learning works and how it improves your workflow
- Practical advice on how to go from notebook to package
- How Python’s Object Oriented features can help you structure your scikit-learn code
- Continuos delivery for Machine Learning
- Monitoring and testing Machine Learning
- Solution Design for Machine Learning applications
- Packaging Machine Learning models and Python code
- Linting and code formatting as part of the CI/CD pipelines
- Testing with pytest
- Creating a command line interface for your packages
- Implementing a REST API on top of your models
Data Science Learning Journey
Julian de RuiterMachine learning engineer
Structured, to-the-point, good combination of theory and practical examples, very knowledgeable trainer who can explain concepts very well
It was a hands-on and tangible course. We could apply what we learned in a matter of minutes. The trainer did a great job of answering ad-hoc questions that complemented the material. We appreciated the fact that we could apply what we were taught directly to our company.
I liked every aspect of this training and would like to thank the trainers. They did an excellent job of explaining how to use Spark for data science. This is the fourth GoDataDriven training I’ve followed. All were great, but this was the best one so far.
Climbing a steep Python and Machine Learning curve in three days. This would have taken me months on my own.