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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 Videos45 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.

  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.

    Play Chapter Now
    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
    Ordering model experimentation + selection
    100 xp
    MLOps best practices
    100 xp
  2. 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.

    Play Chapter Now
    Designing reproducible experiments
    50 xp
    ML reproducibility
    50 xp
    Identifying Key Aspects for Reproducibility and Reliability
    50 xp
    Sorting Reliability Elements
    100 xp
    Sorting Aspects of Reproducibility and Documentation Practices
    100 xp
    Feature engineering
    50 xp
    Classifying Feature Engineering Goals & Techniques
    100 xp
    Identifying Data Manipulation and Transformation Techniques
    50 xp
    Feature selection
    50 xp
    Data and model versioning
    50 xp
    Model store
    50 xp
    Versioning training data
    50 xp
    Ensuring reproducibility
    100 xp
  3. 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.

    Play Chapter Now
    Packaging ML models
    50 xp
    Goal of serialization and containerization
    50 xp
    Model packaging order
    100 xp
    Scalability
    50 xp
    Scaling
    50 xp
    Model complexity
    100 xp
    Categorizing scaling techniques
    100 xp
    Automation
    50 xp
    Benefits of continuous delivery
    50 xp
    CI/CD steps
    100 xp
    Putting ML models into production
    100 xp
  4. 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.

    Play Chapter Now
    Model reliability
    50 xp
    Categorizing Testing Strategies for ML Pipeline Readiness and Staleness
    100 xp
    Addressing Model Staleness in Machine Learning Pipelines
    50 xp
    Testing data
    50 xp
    Picking the right test
    50 xp
    Consistency check
    50 xp
    Testing models
    50 xp
    Predicting customer lifetime value
    50 xp
    Classifying fairness
    100 xp
    Testing machine learning pipelines
    100 xp
    Congratulations!
    50 xp

In the following tracks

MLOps Fundamentals

Collaborators

Collaborator's avatar
George Boorman
Collaborator's avatar
Arne Warnke
Collaborator's avatar
Joanne Xiong

Prerequisites

MLOps ConceptsSupervised Learning with scikit-learn
Sinan Ozdemir HeadshotSinan Ozdemir

Data Scientist, Entrepreneur, and Author

Sinan Ozdemir is a Data Scientist, Entrepreneur, Teacher, and Author. He is the founder of LoopGenius, a company that helps people get their first 100 customers for their side hustle. As an expert in NLP technologies, including LLMs and ChatGPT, Sinan is passionate about exploring the power of these technologies to help businesses achieve their goals. He specializes in generative AI, including stable diffusion and GPT3, and is committed to helping companies rapidly prototype and implement these cutting-edge technologies. Sinan has authored 5 books on Machine Learning and Data Science and has taught at renowned institutions like Johns Hopkins University, O’Reilly, and Pearson.
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