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DataCamp

Developing Machine Learning Models for Production

via DataCamp

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Overview

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

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.

Syllabus

  • 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.
  • 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.
  • 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.
  • 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.

Taught by

Sinan Ozdemir

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