Originally reported by CDOTrends. Original announcement circa March 2020.
What Are Google Cloud Machine Images?
Google Cloud introduced a feature called Machine Images designed to make it significantly easier for system administrators and data scientists to set up, replicate, and maintain specialized virtual machine (VM) environments in the cloud.
At its core, a machine image stores all the information required to fully restore a Compute Engine VM instance. This goes beyond what Google Cloud’s older custom images feature offered. While custom images are limited to a single boot disk, machine images capture:
- All attached disks (boot and data disks)
- Instance configuration properties
- Instance metadata
- Associated IAM permissions
This comprehensive snapshot means a machine image represents a complete, reproducible environment — not just an OS image.
Why This Matters for Data Science and DevOps Teams
Data science workloads typically require carefully configured environments: specific library versions, GPU drivers, mounted datasets, and fine-tuned system settings. Manually reproducing these environments across multiple cloud instances is time-consuming and error-prone.
Machine images eliminate this friction. Once a VM has been configured to the desired state, teams can capture it as a golden machine image and use it as the authoritative baseline for spinning up additional instances — all configured identically to the source.
According to Ari Liberman, a product manager for Google Compute Engine, machine images are designed to sit at the heart of scalability, backup, and disaster recovery strategies. They use the same differential disk backup technology that powers incremental snapshots, making instance backups both reliable and cost-effective.
The “Golden Image” Workflow
Liberman illustrated the intended workflow with a practical example:
“Imagine that you’ve created and configured an instance as part of your web application, and you want to use it as the basis of other instances. With machine images, you can capture your web application exactly as you want it and save it as a golden machine image. You can then use that machine image to launch as many instances as you need, configured in the exact same way as your source instance. You can also share the machine image with other projects.”
Ari Liberman, Product Manager, Google Compute Engine
While the example references a web application, the same pattern applies directly to data science and ML engineering contexts — for example, capturing a configured Jupyter environment with CUDA drivers and Python dependencies installed, then sharing it across a team or replicating it for parallel experiment runs.
Override Functionality: Flexibility Without Sacrifice
Machine images also support override functionality, allowing individual properties of new VM instances to be selectively modified at launch time. This means teams can use a shared golden image as a base while still customizing things like machine type, network tags, or startup scripts on a per-instance basis — without altering the source image itself.
Key Use Cases
- Scalability: Rapidly launch multiple identical VM instances from a single source image to handle increased workloads.
- Backup and recovery: Capture point-in-time snapshots of full VM environments, including all attached disks, for reliable disaster recovery.
- Environment standardization: Share a validated, reproducible VM configuration across teams and projects.
- Onboarding: Provide new team members with a pre-configured environment instantly, reducing setup time.
Machine Images vs. Custom Images vs. Snapshots
| Feature | Custom Images | Snapshots | Machine Images |
|---|---|---|---|
| Scope | Single disk | Single disk | Full VM (all disks + config) |
| Instance metadata | No | No | Yes |
| IAM permissions | No | No | Yes |
| Multi-disk support | No | No | Yes |
| Differential backup | No | Yes | Yes |
Note: This comparison reflects the state of these features as of the March 2020 announcement. Google Cloud feature capabilities may have evolved since then.
How to Access Machine Images
At the time of announcement, Machine Images were available in beta and could be accessed through:
- The Google Cloud Console
- The gcloud CLI
- The Compute Engine REST API
For the current status of this feature and up-to-date documentation, refer to the official Google Cloud Machine Images documentation.