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Developing Machine Learning Models for Production

Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.

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4 Hours13 Videos44 Exercises
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Course Description

Much of today’s machine learning-related content focuses on model training and parameter tuning, but 90% of experimental models never make it to production, mainly because they were not built to last. In this course, you will see how shifting your mindset from a machine learning engineering mindset to an MLOps (Machine Learning Operations) mindset will allow you to train, document, maintain, and scale your models to their fullest potential.

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.

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  1. 1

    Moving from Research to Production

    Free

    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.

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    Adopting an MLOps mindset
    50 xp
    What is a key aspect of MLOps?
    50 xp
    What is technical debt?
    50 xp
    Writing maintainable ML code
    50 xp
    Version control
    50 xp
    Code organization
    50 xp
    Writing effective ML documentation
    50 xp
    Why document labeling methods?
    50 xp
    MLOps best practices
    100 xp
  2. 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.

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  3. 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.

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In the following tracks

Machine Learning EngineerMLOps Fundamentals

Collaborators

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George Boorman
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Arne Warnke
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Joanne Xiong
Sinan Ozdemir HeadshotSinan Ozdemir

Data Scientist, Entrepreneur, and Author

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