G2

Sonu P.

Design engineer

Mid-Market (51-1000 emp.)

8/26/2026

"Simplifies deep learning environment setup"

5/5

What do you like best about Deep Learning VM Image?

What I like most about the Deep Learning VM Image is how much setup time it saves. Having the necessary ML frameworks, GPU drivers, and development tools already configured makes it much easier to jump straight into deep learning projects. The environment is straightforward to use, and performance is solid when running GPU-based workloads. I also appreciate the flexibility to work with different AI and machine learning frameworks without spending hours setting everything up manually. Overall, it has made my workflow more efficient, especially when I’m testing models and experimenting with different workloads. Review collected by and hosted on G2.com.

What do you dislike about Deep Learning VM Image?

One area that could be improved is the initial setup and configuration experience. Some users may still need to spend time understanding the available frameworks, GPU settings, and integrations before getting everything running smoothly. Documentation and onboarding could be more straightforward, especially for new users. Pricing can also become a consideration for longer-running GPU workloads. Better setup guidance, clearer cost information, and simpler configuration options would make the overall experience easier and more cost-effective. Review collected by and hosted on G2.com.

What problems is Deep Learning VM Image solving and how is that benefiting you?

Before using Deep Learning VM Image, we spent quite a bit of time setting up the environment, installing frameworks, and configuring GPU-related dependencies. Now we can get a working deep learning environment ready much faster and focus more on testing and running models. It has reduced setup effort, made our workflow more consistent, and helped us spend more time on actual development instead of troubleshooting configuration issues. Review collected by and hosted on G2.com.