Easy and fast result caching for machine learning pipelines

This is the Part 1 of the series: Building fast and efficient lightweight machine learning pipelines using Joblib.

Let’s begin our journey of developing machine learning pipelines using Joblib. In this article, we will see a “comparative study of result caching with Joblib” along with code.

Journey beginning — SpaceX
Journey beginning — SpaceX
Credits: Photo by SpaceX on Unsplash

Outline

  1. Market need
  2. Ways of reducing computational time
  3. Why use Joblib?
  4. Ways of caching the result in Joblib
  5. Faster cache lookup — reducing total execution time further
  6. Clearing cache
  7. Summary — comparison of different caching methods
  8. Before you go — complete code link and next in the line

Feel free to jump to any section…

Karan Pathak

Finding my way in ML. Connect with me: https://karanpathak.github.io/

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