Reproducibility in ML - Learn Data Science with Travis

Reproducibility-in-ML

Reproducibility in machine learning (ML) refers to the ability to replicate the results of a model or experiment consistently. It is a critical aspect of scientific research and development, ensuring that findings can be verified and built upon by others. In ML, reproducibility involves not only obtaining the same results when running the same code with the same data but also ensuring that the underlying processes and methodologies are transparent and accessible. Challenges such as version control, data management, and the complexity of ML frameworks can hinder reproducibility, making it a significant concern in both academic and industrial settings.

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…

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...

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...

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.

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...

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...

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...

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…

Wrapping up the Papers with Code ML Reproducibility Challenge — Spring 2021

Towards Data Science

Data Science Reproducibility is one of the core reasons DagsHub was established, and we’re constantly developing new tools and integration to support a fully reproducible workflow. This is why we’re…

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 Machine Learning

Towards Data Science

The NeurIPS (Neural Information Processing Systems) 2019 conference marked the third year of their annual reproducibility challenge and the first time with a reproducibility chair in their program…