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DagsHub
14 reviews
- 1 profiles
- 6 categories
Average star rating
4.8
Serving customers since
DagsHub Reviews
GB
Gourav B.
Senior Data Scientist@Allianz | Technical Content Writer | Evaluator at GreatLearning | Contributor at O'Reilly | Contributor at Netptune.ai | Published 100+ articlesSimplifies LLM Dataset Versioning and Experiment Tracking
DagsHub makes it easy to manage the complex data pipelines required for training and fine-tuning large language models. We use it to version instruction datasets, evaluate prompt variations, and track model performance over time. Having a single source of truth for data, experiments, and models has been a game-changer for reproducibility. The Git-like workflow for data is intuitive, and it integrates smoothly with our existing tools.
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Nilesh B.
Founder of PerceptronAI and Deep Learning engineerBest Platform for Managing LLM Training Data and Experiments
DagsHub is ideal for managing large language model (LLM) training data. We use it to version curated text corpora, track data cleaning steps, and run experiments on fine-tuned models—all with full reproducibility. The ability to connect datasets directly to experiment runs and model outputs helps us stay organized and iterate quickly. It’s also helpful for collaborating across teams working on data prep, prompt engineering, and evaluation.
UC
Verified User in Computer Software
End-to-end control of ML data and experiments
DagsHub gives me full control over my machine learning data and experiments. I can version raw datasets, preprocess them, track experiments, and manage model outputs, all in one place. This tight integration means less time syncing tools and more time improving my models. It’s especially useful for unstructured data like images and documents, where traceability and collaboration are hard to maintain without the right setup.
About
What is DagsHub?
DagsHub Inc is a platform designed to facilitate collaboration in data science and machine learning projects by using a combination of popular open-source tools and a user-friendly interface. It integrates with Git to provide version control for data and code, and supports reproducibility and transparency in project workflows. DagsHub enables users to manage datasets, track experiments, and visualize results, fostering collaboration among team members. The platform also supports community engagement by allowing data scientists to share their projects and findings with a broader audience.
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