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.

- Adding tags to multiple executions at once.

- Alias for carrying outputs from one pipeline to another.

- Ease of logging

- Reusing nodes

- Traceability

- Multiple pipelines in the same YAML Review collected by and hosted on G2.com.

- Creating pipeline:

* When nodes have an alias as default input, the alias does not load automatically.

- Tags:

* Can't add tags to the triggers in the UI

* Can't add tags to the pipeline in the UI after it's created Review collected by and hosted on G2.com.

- Tech agnostic

- Awesome support team

- Compatible with our security requirements

- Allows our AI team to focus on developing DL models into production without heavy collaboration with DevOps

- Centralized place for all of our data science experiments, models, and metrics.

- Platform allows us to achieve our goals of automating as much as we can Review collected by and hosted on G2.com.

- There has been a very minor bug or two that was resolved quickly by communicating with their support team.

- Plotting functionalities may be minimal, but their tech agnostic approach allows you to integrate W&B to get the best of both worlds for example. Review collected by and hosted on G2.com.

I liked the way it easily integrates different tools in a single UI that makes it easy for the user to run anything from simple scripts to notebooks to entire GitHub repos from a single Dashboard which allows you to easily keep track of the resources used and the parameters set for each run. In my case, I liked the way I could have a bird's eye view on all the training pipelines for my ML models, their performances in terms of accuracy, speed and resources needed to run. It also very smoothly integrates with our DBs and s3 buckets. Review collected by and hosted on G2.com.

So far, nothing. All the issues I had were promptly taken care of by the Valohai team so that I could run my ML tools (data harvesters, model training and data cleaning pipelines) without any major disruption. Review collected by and hosted on G2.com.

Their customer service is amazing, prompt, responsive, and most importantly, effective Review collected by and hosted on G2.com.

Their new colors on their home screen, would prefer a dark mode way more. Review collected by and hosted on G2.com.

* Complete technology agnostic

* Catering for different expertise and commitment, from simply running an experiment in the UI, to building a pipeline with hundreds of steps programmatically Review collected by and hosted on G2.com.

Nothing really disliked. I wish the flow control in pipelines, e.g. if or while loops, would be more sophisticated, but it is something that they are constantly working on. Review collected by and hosted on G2.com.

The cost savings, we were considering building an on-prem datacenter but with valohai we get per/second charging of AWS ec2 instances and you just can't beat tho bottom line cost

The traceability, being able to trace and reproduce every production model is invaluable to us. working with consumer products it just makes sence. for our automotive customers it's a must have

The customer support, knowledgable personnel who has never let us down. And still to this date implement customer requests and suggestions

The generic way it's built. we do alot of things in valohai. not just Machine learning. like rendering or data processing. if we have code that we want to run on a cloud machine there's really no reason not to run it through valohai. Review collected by and hosted on G2.com.

Often times we use Valohai in ways it was most likely not intended for. this means that we sometimes stumble upon undocumented behavior, rules or limitations.

it's not always clear if it's a valohai limitation or user error or bug due to no- or hard to understand error codes

this really isn't valohais fault, and we have always gotten superb support whenever these issues occur. Review collected by and hosted on G2.com.

Valohai is very intuitive to use, which made it easy for our team to deploy and monitor models efficiently and effectively. Review collected by and hosted on G2.com.

There were a few minor quality of life features that were missing, but the Valohai team listened to our feedback anytime we discussed feature requests. Review collected by and hosted on G2.com.

Best things about Valohai:

- easy to use, great documentation, active support from their team

- covers most important ML workflows: model and experiment versioning, model deployment, hyper-parameter search

- smooth Github code and cloud compute integrations Review collected by and hosted on G2.com.

Not much to dislike. On a few rare occasions, we reached the limits of their available functionality, but always got help from their team to find a work-around or had the functionality available in a later release. Review collected by and hosted on G2.com.