Course
Developing Machine Learning Models for Production
IntermediateSkill Level
Updated 11/2024Start Course for Free
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TheoryMachine Learning4 hr13 videos44 Exercises2,850 XP8,088Statement of Accomplishment
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Experiment and Document with Ease
Experimenting with ML models is often enjoyable but can be time-consuming. Here, you will learn how to design reproducible experiments to expedite this process while writing documentation for yourself and your teammates, making future work on the pipeline a breeze.Build MLOps Models For Production
You will learn best practices for packaging and serializing both models and environments for production to ensure that models will last as long as possible.Scale Up and Automate your ML Pipelines
By considering model and data complexity and continuous automation, you can ensure that your models will be scaled for production use and can be monitored and deployed in the blink of an eye.Once you complete this course, you will be able to design and develop machine learning models that are ready for production and continuously improve them over time.
Prerequisites
MLOps ConceptsSupervised Learning with scikit-learn1
Moving from Research to Production
This chapter will provide you with the skills and knowledge needed to move your machine learning models from the research and development phase into a production environment. You will learn about the process of moving from a research prototype to a reliable, scalable, and maintainable system.
2
Ensuring Reproducibility
In this chapter, you’ll learn about the importance of reproducibility in machine learning, and how to ensure that your models remain reproducible and reliable over time. You’ll explore various techniques and best practices that you can use to ensure the reproducibility of your models.
3
ML in Production Environments
In Chapter 3, you’ll examine the various challenges associated with deploying machine learning models into production environments. You’ll learn about the various approaches to deploying ML models in production and strategies for monitoring and maintaining ML models in production.
4
Testing ML Pipelines
In the final chapter, you’ll learn about the various ways to test machine learning pipelines and ensure they perform as expected. You’ll discover the importance of testing ML pipelines and learn techniques for testing and validating ML pipelines.
Developing Machine Learning Models for Production
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