Valohai Reviews & Product Details

The Valohai platform truely enables collaboration by ensuring transparency and traceablilty of data and models and by being fully integrated into version control.

- The whole team can access and inspect experiments.

- Changes can be easily implemented and tested.

- Individual executions are highly customizable, allowing efficient and ECONOMICAL resource use.

- Ability to reuse "good" or unchanged steps in a pipeline; saves time!

- Comprehensive documentation making it very easy to get your first implementation up and running.

- Incredible flexibility and outstanding customer support... if I ever had trouble getting something to run, the solution was only a quick chat, personalized video, or one-on-one debug session away.

Valohai is now my daily go-to platform for ML projects. Review collected by and hosted on G2.com.

Sometimes debugging a Valohai-specific feature can inflate your git commit history... when hunting down the ever-elusive one character bug. But thanks to that I learned about git squash! So really no problem in the end :D Review collected by and hosted on G2.com.

The platform is very straightforward and easy to use, and the UI is accessible to a wide range of users regardless of their technical expertise. It's easy to get started and learning its intricacies doesn't take long at all. Just write some yaml, store some environment variables, connect to your repo, and go to town on your projects.

In terms of collaboration features, it's not lacking, as a team we can work on shared workspaces meaning all the people involved in the same project can access and work on the same experiments. Due to how it integrates with Git, it also provides version control and traceability. It's incredibly easy to share setups with other team members as anyone can go and review, debug, or replicate previously set up tasks or pipelines. This also enables a collaborative workflow between data scientists and data engineers, where we can contribute to the different stages of the project at the same time, which streamlines the dev process.

It has an efficient hyperparameter tuning setup making it a useful tool for fine-tuning. No matter your flavor of framework, whether you're team PyTorch or team Tensorflow, the support for multiple frameworks ensures you don't have to make significant changes to your tech stack.

When you define the parameters for your tuning run, it immediately gives you a number of how many combinations your parameters result in, which is really handy as it enables users to be conscious about the number of runs and costs associated with them. In the cases where you need to do heavy grid searches, the auto-scaling queue handles all the runs, which is one less thing you have to worry about.

The team behind Valohai is incredibly lovely and the customer support is knowledgeable, friendly, and responsive. I really like that they encourage us to get in touch directly with them whenever we come up with any issues. They're great at troubleshooting the issues we encounter and are quick to offer solutions that work. Review collected by and hosted on G2.com.

Not necessarily a dislike, but I'd like to see more documentation or examples of how to run things in a notebook and how to capture the results of notebook runs. Review collected by and hosted on G2.com.

- Easy to use, understand and setup either recurring to the UI or the command line tools

- Very good documentation

- Excellent customer support, always eager to improve in the smallest of details

- Flexible and easy to integrate with other solutions such as HF, W&B

- Experiment tracking and reproducibility at its finest Review collected by and hosted on G2.com.

- The tags of the experiments in one step are not directly ported to the downstream steps Review collected by and hosted on G2.com.

It is very easy to use and has a straightforward UI. Valohai makes building pipelines an easy and enjoyable process. Most importantly, the support from the Valohai team is amazing. They are responsive and friendly. Review collected by and hosted on G2.com.

Nothing encountered so far; it is very straightforward to use. Review collected by and hosted on G2.com.

The platform offers excellent functionality for Machine Learning Pioneers, especially those interested in quickly iterating and deploying models. Also, the platform's data traceability functionality is outstanding, allowing you to track and audit all changes to your data and models, ensuring transparency and reproducibility.

The platform provides cost-saving opportunities by utilizing cloud-based resources, allowing for infrastructure optimization and scalable computing power, which is precious for organizations with tight budgets.

The customer support is fantastic, assisting at all levels. Whether you encounter technical issues or need guidance on best practices, their support team is always ready to help.

It is an all-in-one solution for ML Pioneers who seek efficiency, cost savings, and reliable customer support. Review collected by and hosted on G2.com.

While Valohai's MLOps platform offers many benefits to users, there may be some aspects that some users may dislike. For example, some may find the platform's complexity challenging to navigate, especially if unfamiliar with MLOps concepts. While Valohai has many strengths, it may not be the ideal solution for every Machine Learning Pioneer. Users should carefully evaluate their needs and preferences before committing to the platform. Review collected by and hosted on G2.com.

ML engineers @Floy use Valohai as backbone for developing and evaluating medical AI for radiology imaging data. Valohai allows us not only a seamless integration in our workflows but also the use of our own compute infrastructure. Additionally, clever solutions for worfklows/pipelines, deployments and data versioning enables us to resolve a lot of required operational requirements directly in the Valohai infrastructure. Review collected by and hosted on G2.com.

With many features still being in development, in rare cases the available API lacks in the desired functionality - however, issues are resolved quickly and feature requests solved promptly. Review collected by and hosted on G2.com.

Very easy to handle MLOps stuff within a company with minimal knowledge. Everything for MLOps is provided in this platform, and no more additional tools are required. Everything, including data, code, and environment. etc., are versioned without extra effort. The idea of pipelines in Valohai makes the development of the progressive life cycle of an ML model easier. They have excellent customer service, are very patient, and are very skilled at the same time. So they encourage you to ask them for help whenever there is an issue. Review collected by and hosted on G2.com.

The dataset concept is exciting in Valohai, making data maintenance more accessible. But it is just available for AWS at the moment. Another thing is the UI; I think it needs to be improved. Moreover, I believe the yearly price should be reduced to a more reasonable amount. Review collected by and hosted on G2.com.

The customer support is incredible: very high quality and availability.

The UI is super nice, all our data scientists love it.

The way it's designed - it's oriented towards ML but you can do anything with it - including data preparation.

Overall very good - boost of productivity is obvious to all data scientists when you know how to use it to its full potential. Review collected by and hosted on G2.com.

Documentation could still be improved - mostly written as blog articles, it's not always easy to know what you can and cannot do on the platform. Review collected by and hosted on G2.com.

We were looking for a structured, yet flexible way to start serving our ML models into production, and Valohai well responded to these needs. It provides structure and automation, without imposing too much overhead and constraints on how the code should be written and organized.

I also highly appreciate the possibility to run scripts and notebooks on our own on-premises server while still tracking executions on Valohai.

The customer support, moreover, is nothing short of excellent! Review collected by and hosted on G2.com.

Sometimes I found it hard to navigate the documentation to locate the information I need. Review collected by and hosted on G2.com.

+ Probably our most responsive vendor. Any issues are troubleshooted and queries are answered amazingly fast, which is a massive boon over open source/DIY alternatives.

+ No forced vendor lock in. Has an API and python utils that can be used when writing software but whole system is fully functional with just one .yaml that does not have to be baked in the code.

+ Flexible compute backend. Use instances from AWS, GCP, Azure with cloud storage from AWS, GCP, Azure or OpenStack Swift. On-prem solution also available.

+ Arbitrary code execution. We do a lot of pre- and post-processing and other very computationally intensive work. The same platform which allows us to create and track ML experiments is flexible enough to host a parallel Monte Carlo simulator that uses the results of those ML models. Review collected by and hosted on G2.com.

- API documentation could be more comprehensive.

- API key management still immature, only user-specific API-keys with no access management.

- No synergy benefits like some other commerical all-in-one solutions. The huge players have some pros in their walled gardens, including some more feature-complete solutions. With Valohai you'll need to add more pieces from other sources, such as model monitoring and labelling and other mix-match infrastructure pieces. That's more of a design choice than a negative, the flipside of the freedom and flexibility they allow. Judge by yourself what you need. Review collected by and hosted on G2.com.