DagsHub Reviews & Product Details

DagsHub is a best friend of Data Scientists and Machine Learning Engineers since it provides not only a version control repository for the code but also for the data artifacts, such as datasets and models. MLOps tools like DVC and MLflow are available for every repository and hosted on DagsHub out of the box so it's extremely easy to start using them right away! This is such a big advantage because, for example, MLflow tracks machine learning models locally by default so you need to set up an MLflow server when working in a team which isn't obvious and DagsHub is real time saver here. As a cherry on top of the cake, DagsHub offers many GBs of free storage for your data artifacts and you will definitely appreciate it if you want to try it out for your project. Overall, DagsHub is an amazing MLOps platform with many more stuff that will make your life so much easier, such as annotation tools, GitHub integration, Jupyter notebook diffs, etc. The DagsHub documentaion is just great but if you need extra help, the DagsHub team is super responsive on their Discord channel. Feel free to check out my DagsHub project where I describe in detail how I used its features for my model cloud deployment pipeline https://dagshub.com/PavloFesenko/gif_analyzer Review collected by and hosted on G2.com.

No complaints, I only wish that I have discovered DagsHub earlier. 😄 Review collected by and hosted on G2.com.

DagsHub provides seamless integration with the data version control tool of my choice, namely DVC; it can be easily used as remote repository for storing large data files, and for storing directories with large amount of files. I also like its integration with Git repository hosting sites, not only GitHub, but also other such services, like GitLab or Bitbucket.

DagsHub repository makes it possible to browse and analyze data files, regardless of whether they are versioned using Git, or using DVC. The visualization of data processing pipeline includes both stages, and outputs / data dependencies.

I have only lightly tried the experiment tracking part of DagsHub, but I like what I have seen so far. DagsHub includes support for both DVC experiments (`dvc exp`) and MLflow experiments tracking.

I have yet to try the data streaming support, or mounting DagsHub storage as S3 filesystem - but it looks like a neat feature. Review collected by and hosted on G2.com.

I haven't notice any major issues so far. The platform is robust, and caters well to our data tracking needs.

I don't like the very strict limitation of the free plan (maximum of 2 people in a team), but I can understand it. DagsHub does offer full version for academia, but it is at request, and it is not automated (using for example using Shibboleth login, like GitLab does it). Review collected by and hosted on G2.com.

DagsHub is super helpful for handling multimodal data like vision, audio, and text. It makes cleaning and organizing unstructured data really easy. The built-in experiment tracking and model management tools help us stay on top of everything. The best part? It’s simple enough for anyone on the team to use. Review collected by and hosted on G2.com.

Honestly, nothing so far—it does exactly what we need. Review collected by and hosted on G2.com.

DagsHub simplifies working with multimodal data by streamlining data transformation, experiment tracking, and model management. Its automation tools enhance labeling efficiency, accelerating workflows. With an intuitive interface, it ensures seamless collaboration across teams. Review collected by and hosted on G2.com.

I haven’t encountered any problems, it’s been a smooth and enjoyable experience. Review collected by and hosted on G2.com.

GAGsHub is where people build data science projects Cover the entire machine learning life cycle, no no DevOps required.We can track experiments.We can label the data and visualize, compare, andnshare our results.With a community of thousands of machine learning professionals,DAGsHub helps large international teams and individuals build projects that advancce audio.We can communicate effectively by having interactive discussions on any experiment or filentake notes on the best model architecture or review a ream members's contribution build a knowledge base for your future self and your team.Close the loop from data to production, faster than ever that's the magic of DAGsHub and start building now. Review collected by and hosted on G2.com.

Users might get an error when trying to push files to DAGsHub while pulling files might work.When trying to load a Label Studio project from DAGsHub Annotations. it fails with the Runtime error. Review collected by and hosted on G2.com.