No need to move your data between platforms, suffer from synchronization issues, break the project structure, or perform tedious tasks. — A lot has been said about the importance of clean data with accurate and consistent labels. The entire data-centric paradigm relies heavily on making data labels more consistent to improve model performance. So why isn’t the data science community quickly adopting this approach…

Data labeling, one of the most critical tasks in the lifecycle of a machine learning project, is currently also one of the hardest to manage, scale, and monitor — and not for the right reasons — Andrew Ng, an AI pioneer, is on a mission to change the AI ecosystem to be more data-centric. Ng is basing his research on data (well, you should’ve seen it coming) collected throughout the years on how ML practitioners improve their model’s performances. …

The Spring 2021 edition of the ML Reproducibility Challenge is officially over, and we have some inspiring projects contributed by the DagsHub community to share! — 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. …

Reduce the time from research to production using various MLOps tools and techniques. — Refactoring a project developed in a notebook to a production-ready state can be challenging and consume our time and resources. At DagsHub, we’ve interviewed data science practitioners and ML engineers from hundreds of companies, trying to get to the bottom of their workflow problems and solutions. …

Over 2 TBs of labeled audio datasets publicly available and parseable on DagsHub — October is over and so is the DagsHub’s Hacktoberfest challenge. When announcing the challenge, we didn’t imagine we’d reach the finish line with almost 40 new audio datasets, publicly available and parseable on DagsHub! Big kudos to our community for doing wonders and pulling off such a fantastic effort in…

Using FDS, an open-source tool, to version control your machine learning project fast & easy — FDS, Fast Data Science, is an open-source tool that makes version control for machine learning fast & easy. It combines Git and DVC under one roof, and takes care of code, data, and model versioning. FDS will help you: Avoid mistakes, by recommending where each file should be tracked, using…

Exploring data science hypotheses using Git Flow — Data Science is a research-driven field, and exploring many solutions to a problem is a core principle. When a project evolves and grows in complexity, we need to compare results and see what approaches are more promising than others. In this process, we need to ensure we don’t lose track…

How many times have you received a raw dataset and conduct the same action to pre-process it? Copy and passed code from different projects and re-used it? For this sake, the ‘NBprocessing’ python package was created. …

In this blog post, I will explore how to perform transfer learning on a CNN image recognition (VGG-19) model using ‘Google Colab’. The model includes binary classification and multi-class classification of leaf images. In parts I and II I’ve shown how to connect ‘Google Colab’ to drive, clone the GitHub…