Reproducibility in ML

Addressing ML’s Reproducibility Crisis

Towards Data Science

You’re probably aware that machine learning (ML) has a reproducibility problem. Hundreds of pre-prints and papers are published every week in the ML space but too many can’t be replicated or…

Introducing the Machine Learning Reproducibility Scale

Towards Data Science

Reproducibility in machine learning is a recurring topic, brought up both in research and industry. Lots of opinions, but no easy way to quantify or provide a standard for it. The Reproducibility Scal...

A Common Misconception About Model Reproducibility

Daily Dose of Data Science

Today I want to discuss something extremely important about ML model reproducibility. Imagine you trained an ML model, say a neural network. It gave a training accuracy of 95% and a test accuracy of 9...

Reproducible Machine Learning Results By Default

Machine Learning Mastery

Last Updated on August 16, 2020 It is good practice to have reproducible outcomes in software projects. It might even be standard practice by now, I hope it is. You can take any developer off the stre...

Building Reproducible Machine Learning Pipelines

Towards Data Science

Reproducibility is the accountability required from businesses to further understand and trust the adoption of Machine Learning into our day-to-day lives. As Machine Learning becomes more…

How Reproducibility Crisis is Eating Away the Credibility of Machine Learning Technology?

Analytics Vidhya

Reproducibility of results has become a big challenge for Machine Learning models. We explore the challenges and possible solutions.

Reproducibility

PyTorch documentation

Completely reproducible results are not guaranteed across PyTorch releases, individual commits, or different platforms. Furthermore, results may not be reproducible between CPU and GPU executions, eve...

On Reproducibility

Towards Data Science

Reproducibility is important to science. A scientific result isn’t considered confirmed until multiple studies have reached the same conclusion. Also, new work is more efficient if it can build on…

Traceability & Reproducibility

Marvelous MLOps Substack

In the context of MLOps, traceability is the ability to trace the history of data, code for training and prediction, model artifacts, environment used in development and deployment. Reproducibility is...

Reproducible ML: Maybe you shouldn’t be using Sklearn’s train_test_split

Towards Data Science

Photo by Jason Dent on Unsplash Reproducibility is critical for robust data science — after all, it is a science. But reproducibility in ML can be surprisingly difficult: The behaviour of your model d...

Code Reproducibility Crisis in Science And AI

Towards AI

Saving AI and scientific research requires we share more images generated by the author using OpenAI’s DALL-E 2 Modern science suffers from a reproducibility problem. Typically, the output of a scien...

When LLMs Roll the Dice — the Reproducability puzzle

Level Up Coding

(Un)fortunately, the output from LLMs is not always reproducible. Chat Completions are non-deterministic by default, which means model outputs may differ from request to request. But why is that and h...