Iguazio Pros and Cons

Iguazio brings super power in the hands of data scientists - with the push of a button, you can leverage your cloud compute for GPU accelerated model training and scale to zero for cost savings once completed. Using Kubeflow based pipelines, Iguazio enables machine learning training and deployment automation, leading to tremendous productivity improvement, and allowing an ML team to scale well beyond its normal capacity. The MLRun library further enhances the team's capabilities by implementing numerous common use cases, such as artifact logging, and experiment tracking. The Iguazio technical support team is second to none - using their deep expertise in Kubernetes, DevOps, machine learning and sofware development, they provide responsive support on a comprehensive list of topics. Review collected by and hosted on G2.com.

Troubleshooting can be simplified by making it easier to capture systems logs and share with the support team. Review collected by and hosted on G2.com.

The experience and interface is the same even if it is deployed in various compute environments, so you can combine different cloud or onprem resources in the same dashboard.

The solution team was helping implementation very efficiently. Review collected by and hosted on G2.com.

There is nothing currently that disturbes me. Additional integrations are always welcome! Review collected by and hosted on G2.com.

It seamlessly takes the model to production by simplifying the data science process and saving cost using business innovation across different usecases. Review collected by and hosted on G2.com.

Nothing as such disliking factors are there as far as my experience. Review collected by and hosted on G2.com.