Reviews from AWS Marketplace
0 AWS reviews
-
5 star0
-
4 star0
-
3 star0
-
2 star0
-
1 star0
External reviews
External reviews are not included in the AWS star rating for the product.
The most impactful ML product in the last 5 years
What do you like best about the product?
This is the rare product that both engineers and researchers love, and it has been transformative for our team's ability to work on large, complex problems together. In particular, the Reports feature has become our main medium for collaboration, almost more essential than Github. It makes it easy to keep a shared ground truth for baselines while enabling everyone to fork their own versions. You can pull in as much or as little of the other team members’ work to your own current workspace and Reports – the filtering by tag, time, etc. makes this easy. We primarily use tags to make runs available with a quick semantic hook.
The ability to create custom visualizations (via Vega) and filter across many runs during sweeps has been very useful. We’ve made everything from embedding projections (tsne/umap) to sweep overviews here, and then been able to share them for everyone to use.
The ability to create custom visualizations (via Vega) and filter across many runs during sweeps has been very useful. We’ve made everything from embedding projections (tsne/umap) to sweep overviews here, and then been able to share them for everyone to use.
What do you dislike about the product?
I was pretty skeptical initially that it would help to have more collaborative visualization tools beyond Tensorboard, etc – and I was completely wrong! I wish I’d realized this sooner, wandb seems to know the current flaws in our workflow better than we do :)
What problems is the product solving and how is that benefiting you?
Wandb has been key for us to find regressions or mistakes that might have taken months to uncover without it (or never found them at all!). Many of the biggest advances in generative modeling that we've seen in the last 5 years (language models, text-to-image) were made by teams using wandb, and I wouldn't be surprised if the field was nearly a year behind its current frontier if wandb didn't exist. Especially for generative modeling, visualization and tracking is so essential that it saves you time you didn't realize you were wasting (both in experimental mistakes and collaboration/communication cost). None of the other tools we've tried (Tensorboard and similar) or experiment tracking systems we've built internally have been nearly as good as wandb for this. Also, logging/experiment tracking/visualization is surprisingly difficult to get right as you scale in team and model complexity, so the fact that wandb is very simple to integrate into any codebase makes one almost forget how much it is handling.
- Leave a Comment |
- Mark review as helpful
Weights and Biases Review Sky Voice Team
What do you like best about the product?
Very clean and easy to understand UI. Easy integration with Tensorflow, it's nice to see metrics per epoch
What do you dislike about the product?
Would be nice if there was model deployment functionality. Also, it would be nice to have a service user option or a team API key. Since our runs are triggered using AWS Sagemaker pipelines, we have had to hardcode one of our team member's user API keys which isn't the nicest solution since he isn't always the person triggering the run yet it's still linked to him.
What problems is the product solving and how is that benefiting you?
We use Weights and Biases to track runs of our model training pipeline. It benefits us by being able to analyse and compare our runs, sorting runs into groups is very useful for experimenting with different model architectures and datasets.
A Must Have Tool if You are a Serious ML Practitioner
What do you like best about the product?
W&B is so user-friendly and useful for any ML practitioner but if you are a serious one, you need to get your hands on this tool. Not only you can monitor the performance of your different architecture changes and hyper-parameters, but you can also debug some of the problems with your training. For example, one time I was pulling my hair understanding why my training is so slow, and just by looking at the system dashboard, I realized that CUDA had failed for some reason and I was training on CPU. The system dashboard is also so helpful to find the right batch size to make use of the last MBs of your VRAM, if you know what I mean ;) . All the different plotting options and model/hyper-parameter comparison capabilities, give you a lot of freedom and power to efficiently train machine learning models.
I also appreciate the fact the product is constantly evolving and adapting in flow with the scene of AI. Their blog posts are also a treasure trove of ML knowledge which shows some top-notch serious ML people are working on the product.
All in all, go try it, it is fun and useful!
I also appreciate the fact the product is constantly evolving and adapting in flow with the scene of AI. Their blog posts are also a treasure trove of ML knowledge which shows some top-notch serious ML people are working on the product.
All in all, go try it, it is fun and useful!
What do you dislike about the product?
The UI has a very small delay in updating the progress of your training which you might find annoying if you are an impatient person. Also, I would have loved it if they could add other features like the estimated time to finish the training or even show the time scales of the training steps on the plots (maybe there is a way to activate it but did not find)
What problems is the product solving and how is that benefiting you?
It takes away the need to write custom tools for monitoring your ML training and gives you the tools and capabilities to make your life a lot easier when you are a serious ML practitioner.
Wandb review
What do you like best about the product?
The simplicity of integrating wandb into our pipelines and experiments
What do you dislike about the product?
Nooooooooooooooooooooooooooooooooooothing
What problems is the product solving and how is that benefiting you?
It takes away a lot of work in regards to versioning artifacts, which makes our team a lot more productive
amazing product to accelerate the whole ML team
What do you like best about the product?
Weights and Biases makes our entire workflow easier. It biases new (and sometimes seasoned!) engineers toward better best practices, makes it easier to introspect, improve, store, and serve models - it makes the entire process better. Can't recommend highly enough!
What do you dislike about the product?
Really can't think of anything. Wish they'd ship even more new features faster; the ones they've added recently are spectacular!
What problems is the product solving and how is that benefiting you?
Weights and Biases makes training, introspecting & improving, storing, and serving models easier. It improves the workflow of our entire machine learning engineering team.
Excellent set of tools for many ML Ops tasks.
What do you like best about the product?
The quality of the features on wandb is always very high. We use metrics, artifact management, and hyperparameter sweep extensively and find that wanbd fits seamlessly with our training.
What do you dislike about the product?
wandb is developing their feature set but doesn't yet have a complete solution to all of ML Ops. That means I still have to find other tools to fill the gaps. Usually wandb integrates well with these tools but the integration always requires work.
What problems is the product solving and how is that benefiting you?
wandb helps me track and manage my ml processing jobs and artifacts. Without them I would have to stitch together multiple other tools or write my own ad-hoc versions of things. wandb definitely helps by automating the most tedious parts of my processing so I can do more high-value work.
Best tool for a Kaggle Data Scientist
What do you like best about the product?
Highly intuitive and simplistic API, also a great starting point for Kaggle starters.
What do you dislike about the product?
There nothing to hate or dislike, as the it does what it say, that too beautifully.
What problems is the product solving and how is that benefiting you?
Deep Learning model training, evaluation and implementation of research papers become very easy with W&B.
We use it for all our projects
What do you like best about the product?
Our main use case for W&B is tracking experiments and sharing them in the team. It's super easy to set up and share experiments.
What do you dislike about the product?
Sometimes the UI is a bit slow, but not slow enough that it breaks our workflow. That's the only improvement area I can think of.
What problems is the product solving and how is that benefiting you?
Tracking and sharing model training experiments. The reports feature is also super useful for collecting the results in one place.
Great product for managing ML
What do you like best about the product?
I like that W&B provides an on-premise solution (in our cloud environment) that allows us to manage our data completely internally. Their python library is easy to use and can integrate quickly with our existing workflows for ML research. Specifically, we're able to automatically collect relevant ML data and display appropriate visualizations to help us find the best models. More generally, W&B lets us better keep track of our models and test various experiments easily.
They have a great reporting feature as well, where we can easily create and share reports relating to our ML experiments. The visualizations also flexible, and we can basically create whatever visuals we want (although with some effort)
I also like that they keep adding more features to help us accelerate and manage all of our ML operations easily. To my knowledge, we haven't made use of all of these features yet (at least Artifacts and Tables), but they will definitely help us with our workflows as we grow and mature our teams.
They have a great reporting feature as well, where we can easily create and share reports relating to our ML experiments. The visualizations also flexible, and we can basically create whatever visuals we want (although with some effort)
I also like that they keep adding more features to help us accelerate and manage all of our ML operations easily. To my knowledge, we haven't made use of all of these features yet (at least Artifacts and Tables), but they will definitely help us with our workflows as we grow and mature our teams.
What do you dislike about the product?
Since we're running on-premise in our cloud, it takes a little effort to maintain the product in our environment.
What problems is the product solving and how is that benefiting you?
Solves where to track machine learning experiments, by providing a single environment for research results to be collected and displayed. This helps our researchers be more productive at doing ML research and finding the best models for our problems.
Essential tool for ML engineering teams
What do you like best about the product?
Wandb allows my team to collaborate and share information. As soon as we became users of the tool I noticed that we would spend time analyzing the training loss graphs for model runs, and asking each other for help. These runs used to be squirrelled away on people's desktop machines, and were nearly impossible to reconstruct old runs. Now we can look at older runs very easily and our team can collaborate on experiment results. The support from the wandb team has been amazing too.
What do you dislike about the product?
nothing really missing in my opinion. I would like to be able to administer my account a little easier, like seeing how many seats I have left and which users are dormant would allow me to manage my license pool more effectively.
What problems is the product solving and how is that benefiting you?
It gets our engineers collaborating more and speeds up the team, Training these models can be very tricky and have more people analyzing the results from a run really helps us to save time and accelerates our development. Also tracking historical runs is very handy too.
Recommendations to others considering the product:
I highly recommend it.
showing 11 - 20