G2

Shiv K.

Maintenance Engineer

Manufacturing

Mid-Market (51-1000 emp.)

8/24/2026

"Fast setup for reliable machine learning workloads"

4.5/5

What do you like best about Deep Learning VM Image?

What I like most about the Deep Learning VM Image is that it provides a ready-to-use machine learning environment without requiring me to spend a lot of time manually setting up drivers, CUDA, Python libraries, and frameworks. Its integration with Google Cloud also makes it easy to connect to other cloud services, and the preconfigured GPU support helps improve model training performance. I also find JupyterLab useful for testing ideas and experimenting with models. Overall, it has sped up my workflow by cutting down on setup and troubleshooting time, and the documentation and Google Cloud support are helpful whenever I need to configure something or track down an issue. Review collected by and hosted on G2.com.

What do you dislike about Deep Learning VM Image?

The main issue is that the initial setup can still feel confusing, especially when choosing the right VM, GPU configuration, and software versions. Compatibility between CUDA, drivers, and different ML frameworks can sometimes require extra troubleshooting. The overall cost can also increase quickly when using GPU instances for longer training jobs, so it would be helpful to have clearer cost guidance and easier options for managing resources. Better onboarding for beginners and more straightforward version management would make the experience easier. 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, setting up the right Python environment, GPU drivers, CUDA, and ML frameworks took a lot of time and often caused compatibility issues. With the preconfigured images, we can get a working environment running much faster and spend more time on model development and testing. The GPU support also helps reduce training time for larger workloads. Integration with Google Cloud makes it easier to use other cloud services, while the available documentation helps with setup and troubleshooting. Overall, it has reduced the time spent on environment setup and made our ML workflow more consistent. Review collected by and hosted on G2.com.