How to speed up local machine learning model development

How to speed up local machine learning model development

Introducing an robust machine learning template. Providing a well-structured generic code base which can be easily tweaked according to your use case. Using recent packages (DVC and MLFlow) to ensure reproducibility of model results and effective model performance tracking.

TL/DR: I’ve developed a package on Github, [ml-template_](https://github.com/eddiepease/ml-template), which speeds up the development of local machine learning models by:_

  1. Providing a well-structured generic code base which can be easily tweaked according to your use case
  2. Using recent packages (DVC and MLFlow) to ensure reproducibility of model results and effective model performance tracking

Motivation for developing package

Although it seems like yesterday, I now started out on my machine learning journey 4+ years ago. In that time, I have been lucky enough to tackle a number of cool problems. From writing an algorithm to detect someone’s gender based on a picture of their shoes, to using natural language processing to predict your next job given your CV to predicting the outcome of clinical trials, I have worked on a wide variety of problems.

These problems are clearly quite different and have their own unique challenges. What interests me for this post, though, is not how these problems are different but what they have in common. Every time I have started coding-up a machine learning problem, I have had to write the same objects and functions to split the training and test sets, train the algorithm, perform cross-validation, save the trained model and so on. This has applied regardless of whether it is a natural language problem, a machine learning vision or any other type of problem.

open-source machine-learning mlops data-science

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