--- title: Google Cloud Deep Learning VM Image Reviews meta_title: 'Google Cloud Deep Learning VM Image Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 51 reviews by the users' company size, role or industry to find out how Google Cloud Deep Learning VM Image works for a business like yours. aggregate_rating: rating_value: 4.3 review_count: 51 scale: '5' date_modified: '2026-09-12' parent_category: name: Deep Learning url: https://www.g2.com/categories/deep-learning --- # Google Cloud Deep Learning VM Image Reviews **Vendor:** Google **Category:** [Artificial Neural Network Software](https://www.g2.com/categories/artificial-neural-network) **Average Rating:** 4.3/5.0 **Total Reviews:** 51 **AI Verified:** At least 10 G2 reviewers have confirmed using this product's AI features and functionality. ## About Google Cloud Deep Learning VM Image Deep Learning VM Image Preconfigured VMs for deep learning applications. ## Google Cloud Deep Learning VM Image Reviews ### 1. Instant, Pre-Configured ML Images That Save Hours of Setup Time **Rating:** 5.0/5.0 stars **Reviewed by:** Nirmal K. | Manager, E-Learning, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** August 19, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** Instant Out-of-the-Box Setup: These images come pre-configured with industry-standard machine learning frameworks like TensorFlow, PyTorch, and MXNet, alongside essential data science tools like JupyterLab, NumPy, and pandas. This eliminates hours of tedious manual dependency management and version matching. **What do you dislike about Google Cloud Deep Learning VM Image?** Cloud Infrastructure Learning Curve: For data scientists lacking cloud engineering backgrounds, navigating Google Cloud's broader infrastructure—such as Identity and Access Management (IAM) permissions, VPC networking, and securely mapping Cloud Storage buckets—can present a steep initial learning curve. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** High Scalability & Flexibility: Because they run natively on Google Compute Engine, users can easily scale their hardware. You can start a small CPU instance for initial data exploration and easily transition to massive multi-GPU instances when it is time for heavy model training.

No Additional Software Cost: The Deep Learning VM Images themselves are completely free to use. Users only pay for the underlying Google Compute Engine compute, storage, and networking resources utilized while the VM is running. ### 2. Fast GPU Training with Deep Learning VM **Rating:** 4.0/5.0 stars **Reviewed by:** Rishabh S. | Software Engineer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through Google using a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** August 07, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** Honestly, what I like most is that it saves me from all the boring, frustrating setup work. I used to lose half a day wrestling with GPU drivers and double-checking that my CUDA and cuDNN versions actually lined up with TensorFlow or PyTorch, and it was always a headache. With the Deep Learning VM, most of that is essentially handled for you. I can spin up an instance, choose the GPU I need, and be training models within minutes instead of wasting an entire afternoon on configuration. It also integrates smoothly with the rest of Google Cloud, so pulling data from Cloud Storage or BigQuery doesn’t turn into another chore. And since Jupyter is ready from the start, I can jump straight in and begin experimenting right away. **What do you dislike about Google Cloud Deep Learning VM Image?** The cost is a big downside—GPU instances add up fast, especially if you forget to shut one down after a training run (which I’ve definitely done more than once, and regretted it when the bill showed up). The pre-installed package versions can also be a bit of a headache if your project needs something slightly different; you still end up doing manual tweaks, which kind of defeats the purpose. And honestly, the sheer number of image and machine-type options can feel overwhelming at first. It took me a while to sort through them and figure out which combination actually made sense for what I was trying to do. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** The biggest problem it solves for me is environment setup and consistency. Before, every time I wanted to train a model, I had to double-check that my GPU drivers, CUDA version, and framework versions all lined up correctly—and that alone could easily take a whole day, especially if something broke halfway through. With the Deep Learning VM, most of that is handled for me, so I can go from “I need to run this” to actually training a model in minutes instead of hours. It’s also been helpful for scaling. When I need more compute power for a bigger job, I can spin up a larger instance rather than being limited by whatever hardware I have locally. And because it ties into the rest of Google Cloud, moving data around or working with storage and other services doesn’t require a bunch of extra setup. Overall, it’s saved me a lot of time and frustration that would otherwise go into infrastructure babysitting. That means I can spend more of my time actually working on the model—or on the problem I’m trying to solve—instead of troubleshooting the environment. ### 3. Preconfigured Deep Learning VM Image Speeds Up GPU-Accelerated AI Work on Google Cloud **Rating:** 4.5/5.0 stars **Reviewed by:** Subhashree S. | Developer, Computer Software, Enterprise (> 1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** July 30, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** What I like best about Google Cloud Deep Learning VM Image is that it comes preconfigured with popular AI and machine learning frameworks, GPU drivers, CUDA, and essential libraries, eliminating the time-consuming setup process. It enables developers and data scientists to start training and experimenting with models quickly while offering seamless integration with other Google Cloud services. Its scalability, reliable performance, and support for GPU-accelerated workloads make it an excellent choice for AI development and research. **What do you dislike about Google Cloud Deep Learning VM Image?** One downside of Google Cloud Deep Learning VM Image is that costs can increase quickly when using GPU-enabled instances for extended periods. The platform also has a learning curve for users unfamiliar with Google Cloud services, and managing dependencies or custom environments may require additional configuration. More built-in cost optimization tools and simplified environment management would make the experience even better. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Google Cloud Deep Learning VM Image solves the challenge of setting up and managing machine learning development environments by providing preconfigured virtual machines with popular AI frameworks, GPU drivers, and dependencies already installed. This significantly reduces setup time, accelerates model development and experimentation, and enables teams to focus on building and training AI models instead of managing infrastructure. For businesses, it speeds up AI project delivery, improves productivity, and provides a scalable environment for handling compute-intensive machine learning workloads. ### 4. Frictionless ML Prototyping with a Ready-to-Go JupyterLab Environment **Rating:** 4.5/5.0 stars **Reviewed by:** Sai G. | Specialist Programmer, Information Technology and Services, Enterprise (> 1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 04, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** What I like most is how frictionless it makes fast prototyping. It comes loaded with all the standard ML tools, libraries, and NVIDIA drivers standard out-of-the-box, so I don't waste half a day setting up my environment every time we start a new project. Plus, having JupyterLab already configured and ready to go over HTTPS makes interactive debugging super smooth. **What do you dislike about Google Cloud Deep Learning VM Image?** The large image size means slightly longer initial boot times and higher storage footprint. Also, if you need a very niche or ultra-bleeding-edge framework version, you still end up having to manually override or update the pre-installed CUDA/PyTorch packages. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Problems Solved: Solves complex GPU driver and ML framework setup and version compatibility issues. How it Benefits Us: Saves hours of environment setup per project, allowing us to go from spinning up a VM to running code in under five minutes. ### 5. Preconfigured Deep Learning Tools That Make Setup Effortless **Rating:** 4.0/5.0 stars **Reviewed by:** Mudit . | Startup founder, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 11, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** I like that it comes with most of the deep learning tools and libraries already configured. It makes setup much easier and lets me spend more time actually working on models instead of dealing with installations. **What do you dislike about Google Cloud Deep Learning VM Image?** The main thing I dislike is that the VM can take some time to set up and start, especially when using larger machine configurations. It can also feel a bit expensive if the VM is left running when it’s not being used. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** It mainly solves the hassle of setting up a deep learning environment from scratch. Having the required frameworks, libraries, and GPU support ready makes it easier to start experiments quickly. For me, it saves setup time and lets me focus more on learning, testing models, and working on projects. ### 6. Out-of-the-Box Deep Learning VM Images That Accelerate My Game Design **Rating:** 5.0/5.0 stars **Reviewed by:** Harshwardhan B. | CEO, Information Technology and Services, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 30, 2026 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about Google Cloud Deep Learning VM Image?** As all images come with their key ML Frameworks, and a lot of tools preinstalled, these help me to train the ML Models and these VM images are optimized for ML tasks, so in my game design, when designing for NPCs, we use these directly. **What do you dislike about Google Cloud Deep Learning VM Image?** They cannot be hosted locally, I mean a backup copy that could be taken would had been better, plus installing a new tool into the image is sometimes hard and networking too **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Google Cloud Deep Learning VM Images help me solve the technical hurdles of building AI run features, allowing me to focus on gameplay rather than infrastructure. Since it gives me a PreBuild of what I should be using. ### 7. Zero infrastructure headache for training deep learning models **Rating:** 4.5/5.0 stars **Reviewed by:** Rohit S. | Cloud Operations Engineer, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** September 02, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** I really appreciate most is how these machine images come fully pre-loaded with all the notoriously difficult GPU drivers and heavy machine learning frameworks right out of the box, letting me completely skip the messy infrastructure setup phase. I constantly rely on launching directly into the built-in notebook integration from the cloud console because the straightforward interface lets me spin up a fresh high-powered instance and start testing complex neural networks immediately. One unexpected benefit was realizing just how deeply optimized the base images are for the underlying compute hardware, which honestly makes our massive data training loops finish noticeably faster than my previous manual configurations ever did. **What do you dislike about Google Cloud Deep Learning VM Image?** The sheer volume of specific image versions and framework combinations to choose from was honestly a bit intimidating when I first started exploring the options during onboarding. It takes some serious trial and error reading through the documentation initially to figure out exactly which image flavor maps perfectly to your hardware needs without accidentally overpaying for compute power you do not actually need. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Before adopting these pre-built images we were literally wasting days of engineering time manually installing drivers and constantly fighting horrible package conflicts just to get a basic testing environment stable enough to run code. Now our entire engineering group can simply click a button to launch identical baseline compute instances, which has totally stopped the endless back and forth of trying to replicate annoying local software bugs across different machines. We collaborate a lot smoother now because everyone is guaranteed to be running the exact same software stack when sharing code or tweaking model architectures. It easily saves us countless hours of frustrating IT configuration every single month and lets our team actually focus entirely on tuning our models to get much better results faster. ### 8. Fast, Ready-to-Go ML Environment with JupyterLab and GPU Support **Rating:** 4.5/5.0 stars **Reviewed by:** Salaheddine B. | Electrical and Instrumentation Supervisor, Oil & Energy, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 29, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** What I like most is how quickly I can get a complete machine learning environment up and running. The pre-installed frameworks, Python libraries, JupyterLab, and GPU support eliminate much of the manual configuration, allowing me to focus on developing, testing, and training models rather than managing the environment. **What do you dislike about Google Cloud Deep Learning VM Image?** The main drawback is that costs can increase quickly, especially when using GPU-enabled VMs for long-running workloads. I also find that customizing specific package or framework versions can sometimes be challenging because of preconfigured dependencies. GPU quota and regional availability can also occasionally limit flexibility. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Google Cloud Deep Learning VM Images solve the problem of having to manually configure machine-learning environments, install frameworks, and manage GPU drivers and dependencies. The preconfigured images allow me to deploy a ready-to-use environment quickly and start developing, testing, and training models with less setup and troubleshooting. This saves time, improves consistency, and lets me focus more on the actual ML workload rather than infrastructure configuration. ### 9. Pre-Configured VM and JupyterLab Let You Start Training Models Fast **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Consulting | Enterprise (> 1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 27, 2026 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about Google Cloud Deep Learning VM Image?** The best thing definitely has to be the pre-configured environment. Not needing to waste hours fighting with NVIDIA drivers, CUDA versions, and PyTorch compatibility is a huge relief. You just spin up the VM and JupyterLab is already there waiting for you, so you can start training models immediately. It saves tons of setup headache honestly. **What do you dislike about Google Cloud Deep Learning VM Image?** The biggest issue is definitely the cost if you forget to stop the VM overnight with a GPU attached, your bill gets crazy expensive real quick. Another thing is package conflicts. Sometimes the pre-installed libraries are slightly outdated, and if you try updating CUDA or PyTorch manually, it can easily break the whole environment dependencies and make a big mess. Also, getting GPU quota approved in certain regions can be pretty annoying sometimes. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** The main problem it solves is the classic "dependency hell" when setting up machine learning stacks. Before this, I spent hours trying to get the right NVIDIA drivers, CUDA toolkits, and PyTorch or TensorFlow versions to match without crashing. It also solves the reproduction issue across different machines. If I need a stronger GPU or a different machine type, I can just spin up a fresh VM in a few minutes instead of configuring everything from scratch again. For me, the biggest benefit is pure time saving. It lets me jump straight into testing data and training models rather than wasting a whole afternoon doing boring devops tasks and fixing broken driver packages. ### 10. Spin Up ML-Ready VMs Fast with Seamless Google Cloud Integration **Rating:** 4.0/5.0 stars **Reviewed by:** Kelly B. | Managing Director, PMO, Non-Profit Organization Management, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 27, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** You can spin up a VM and start working right away, without spending hours installing and configuring ML software. PyTorch, TensorFlow, scikit-learn, CUDA, cuDNN, NVIDIA drivers, and JupyterLab are already available out of the box. Overall, the VM fits naturally into the broader Google Cloud ecosystem and feels easy to integrate with the rest of your setup. **What do you dislike about Google Cloud Deep Learning VM Image?** The overall cost is a concern, especially when using GPUs. GPU instances can become expensive very quickly if they’re left running continuously. I’ve also found that updating one component can potentially break another, so it takes extra care to manage dependencies. On top of that, there’s an ongoing need to monitor image and framework versions, keep up with security patches, and watch for deprecated versions. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Google Cloud Deep Learning VM Image solves the hassle of manually building, configuring, and maintaining a machine-learning environment. It offers a preconfigured setup with commonly used ML frameworks, libraries, and GPU drivers, so we can get up and running much faster. The biggest benefit for us is the time savings and productivity boost: instead of spending hours troubleshooting software, drivers, and dependency compatibility, our team can focus on developing, testing, and training models. It also provides access to scalable GPU resources when we need them, which can significantly reduce model-training time compared with relying on local hardware. ### 11. Preconfigured AI Stack and Strong GPU Performance That Accelerates Model Training **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 18, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** I like that Google Cloud Deep Learning VM Image comes preconfigured with tools such as PyTorch, TensorFlow, CUDA, and NVIDIA drivers, so I can start training models without spending hours resolving dependencies. The integration with Google Cloud Storage and Vertex AI also makes it easier to move datasets and models between development and production. Performance is another strong point, especially when using GPU-backed VM instances for larger training workloads. Overall, the reduced setup and maintenance effort makes the platform feel like a good ROI for teams working on AI projects. **What do you dislike about Google Cloud Deep Learning VM Image?** One downside is that the initial setup can still feel overwhelming for users who are new to Google Cloud, especially when choosing the right GPU, machine type, and storage configuration. The preinstalled AI libraries are convenient, but version or dependency conflicts can occasionally require manual troubleshooting. Pricing can also become difficult to predict when running GPU instances for long training jobs, particularly for smaller teams with limited budgets. More guided onboarding and clearer cost estimates would make the experience easier. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Google Cloud Deep Learning VM Image solves the time-consuming setup of AI environments by providing preconfigured tools like TensorFlow, PyTorch, CUDA, and NVIDIA drivers. This means I can launch a GPU-based VM and start training or testing models ### 12. Easy Setup with ML Tools and Effortless Resource Scaling **Rating:** 5.0/5.0 stars **Reviewed by:** Anjan S. | Health Administrator, Hospital & Health Care, Enterprise (> 1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 10, 2026 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about Google Cloud Deep Learning VM Image?** I like that it’s easy to set up and that it comes with many useful machine learning tools already configured. It also makes it simpler to scale resources up or down when needed, which is really helpful as requirements change. **What do you dislike about Google Cloud Deep Learning VM Image?** Setup can still feel a bit technical for beginners, and overall cloud costs may rise depending on the resources you provision and how much you use them. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** It makes it much easier to set up a ready-to-use environment for machine learning and deep learning projects. It saves me time because the required tools and frameworks are already available from the start, so I can focus on the work instead of spending time on setup. ### 13. Easy and Fast AI/ML Setup **Rating:** 3.5/5.0 stars **Reviewed by:** Gaurav G. | HR, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 12, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** What I like best about Google Cloud Deep Learning VM Image is that it makes setting up a deep learning environment much easier. It comes with many of the commonly needed tools and frameworks already configured, so there’s less time spent dealing with installations and compatibility issues. I also like that it can be customized based on the project’s requirements and works well with Google Cloud’s GPU resources **What do you dislike about Google Cloud Deep Learning VM Image?** The main thing I dislike about Google Cloud Deep Learning VM Image is that it can feel a bit heavy and complicated to set up, especially for beginners. There are also quite a few pre-installed packages and dependencies, which can make the environment harder to customize and sometimes lead to compatibility issues. It would be better if the setup were more lightweight and straightforward. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Google Cloud Deep Learning VM Image solves the problem of setting up an AI/ML environment manually. It comes with popular ML frameworks, drivers, and tools pre-installed. For me, this means less time spent on configuration and troubleshooting, faster project setup, and more time focusing on training models and experimenting with AI ### 14. Google CLOUD VM Images Make AI/ML Environments Fast and Hassle-Free **Rating:** 4.5/5.0 stars **Reviewed by:** Jagadis P. | Product Specialist (Order to Cash), Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 09, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** Being in retail organization where we have to work on consumer insights on millions of data points, it takes a lot of AI/ML capacity. And with army of data scientist we have to provide them all the environments to run various algorithms to get the best outcome. Google CLOUD VM image help us to have all the various setup like LINUX, TENSORFLOW,Open CV, ML libraries etc. and all this can be done without installation of all these setup. UX is better where we can operate all possible flows. **What do you dislike about Google Cloud Deep Learning VM Image?** Infra is the biggest problem for this. Its best, but we have to take the full headache of managing VM - Security, patching etc. everything falls on you. Same way - framework lifecycle management also is organizatioin headache. Since it is operated for huge amount of data set, GPU cost can go super expensive. Security is shared responsibility so it all comes on your organization and regular updates are important **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** As a retail organization we have to be super efficient in planning our supplychain and thats where we use it for Demand forecasting according to seasonal ask, according to festive as per region, as per high surge request etc. We also use this for intelligent product recommendation to customer as per user buying patterns. We also started using for improving our shelf availability which is always problem for major retailer. ### 15. Preconfigured Deep Learning VMs That Speed Up TensorFlow, PyTorch, and Jupyter Workflows **Rating:** 4.5/5.0 stars **Reviewed by:** Komal S. | Technical Trainer, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 04, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** What I like best about Google Cloud Deep Learning VM Images is that they provide a preconfigured environment with popular AI and deep learning frameworks, eliminating time spent on setup and dependency management. From my experience delivering AI training and building proof-of-concept solutions, this allows me to quickly start experimenting with TensorFlow, PyTorch, and Jupyter notebooks, making hands-on learning and model development much more efficient. **What do you dislike about Google Cloud Deep Learning VM Image?** One downside of Google Cloud Deep Learning VM Images is that they can become expensive if GPU-enabled instances are left running unintentionally. Additionally, while the preconfigured environments are convenient, customizing them for specific project requirements or newer framework versions can sometimes require extra configuration and maintenance. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Google Cloud Deep Learning VM Images solve the time-consuming process of configuring AI development environments from scratch. With pre-installed frameworks, GPU drivers, and development tools, I can quickly provision an environment for model development, demonstrations, and hands-on training. This saves setup time, reduces configuration issues, and allows me to focus on building AI solutions and delivering practical learning experiences. ### 16. Fast, Ready-to-Use ML Environments with Seamless Google Cloud Integration **Rating:** 5.0/5.0 stars **Reviewed by:** Jamil I. | SPS associate, Enterprise (> 1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 07, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** What I like most is how quickly I get a machine learning environment up and running without spending time configuring everything manually. Most of the common frameworks and GPU drivers are already setup, so I can start experimenting almost immediately. It also ingrates well with other google cloud services, making it convenient to manage projects and scale resources when needed. **What do you dislike about Google Cloud Deep Learning VM Image?** The learning curve is a bit steep for beginners and GPU costs can add up quickly if you're not careful. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** It reduces the time spent setting up deep learning environments. I can focus more on building and testing modules instead of dealing with installation and configuration issues. ### 17. Deep Learning VM Images: GPU-Ready, Compatible Stacks in Minutes **Rating:** 4.0/5.0 stars **Reviewed by:** Luca P. | Chief Operations Officer DEQUA Studio | Formerly CTO in MarTech, Marketing and Advertising, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** July 26, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** The pitch is simple and it holds up in practice: a Compute Engine VM that boots with the framework, the CUDA stack, the NVIDIA driver, and the Python environment already installed and already compatible with each other. That last part is the one that matters. Anyone who has assembled a training machine by hand knows the real time sink is not installing PyTorch, it is discovering that the driver you installed does not match the CUDA toolkit that the framework wheel was compiled against, and unwinding that at 6pm. The Deep Learning VM images ship with those pieces tested together, and in the time I have used them I have not once had to debug a driver and toolkit mismatch on a fresh instance. That is the whole value proposition in one sentence, and it delivers. Provisioning itself takes minutes. I launch either from the Cloud Marketplace listing, where I pick the framework, the machine type, and the GPU from a form, or from gcloud when I am scripting it. The image family naming is worth learning because it encodes exactly what you get: framework, CUDA version, and OS in the family name, so I can pin a specific combination for reproducibility or point at the latest tag in the family when I just want current. For a team that needs everyone training against the same stack, referencing one image family in a shared launch script settles the environment question before it starts. What actually comes preinstalled is broader than the framework alone. On top of PyTorch or TensorFlow the image carries a full working Python data stack, the kind of baseline that every project needs and nobody enjoys assembling: - numpy, scipy, and pandas for the data handling layer - scikit-learn and scikit-image for classical ML and preprocessing - matplotlib for the quick sanity-check plots during training - OpenCV and Pillow for image pipelines - JupyterLab, running and reachable, for interactive work The JupyterLab piece deserves its own sentence. It is preconfigured on the instance, so the path from creating a VM to typing into a notebook against a live GPU is genuinely short. When I want to prototype a data loader or eyeball a batch of augmented samples before committing to a long run, I am in a notebook on the actual training hardware within a few minutes of deciding I need one. No SSH tunnel gymnastics, no installing a kernel by hand. GPU support is the area where the preconfiguration earns its keep most visibly. Attach a GPU at creation time and the image handles the driver, CUDA, cuDNN, and NCCL. On multi-GPU machines NCCL being present and matched to the CUDA version means distributed data parallel training works on the first attempt instead of after an afternoon of version archaeology. I have gone from no infrastructure to a multi-GPU PyTorch DDP run in under an hour, and most of that hour was my own code, not the machine. Because these are ordinary Compute Engine instances underneath, everything I already know about GCE applies. Snapshots for saving a configured state, persistent disks I can detach and reattach to a bigger machine when a job outgrows its instance, Spot provisioning for workloads that tolerate interruption, per-second billing so a two-hour experiment costs two hours. The image adds the ML layer without taking anything away from the platform layer, and that composability is what makes it usable for real work rather than demos. The update cadence is steady. New image versions track framework releases at a reasonable distance, and pinning a family version keeps older projects stable while new projects start on current builds. The images are also free in themselves. You pay for the compute, the disk, and the accelerator, and the preconfigured environment costs nothing on top, which makes the decision to use one over a bare Debian image very easy. The same environments also exist as Deep Learning Containers, and the pairing turns out to be more useful than it sounds. I prototype on a VM image, and when the workflow graduates to something scheduled or orchestrated, the matching container carries an equivalent stack into whatever runs it. Moving a training job from an interactive VM to an automated pipeline without rebuilding the environment from scratch removed a step I used to dread, because that rebuild was historically where subtle version drift crept in. Access to the machine is unremarkable in the best way. SSH from the console works in the browser with no key ceremony when I am away from my own laptop, gcloud compute ssh handles it from a terminal, and port forwarding to the JupyterLab instance is one flag. None of this is unique to these images, but it means the surrounding workflow has no rough edges of its own, and the two minutes saved per session compound over a project. One more thing I did not expect to care about: the base and HPC image variants without a framework. When I need a machine with the CUDA plumbing done but want to bring my own environment through conda or a container, the base image gives me the annoying bottom layers solved and stays out of the way above that. It is the version of the product for people who have opinions about their Python setup, and I appreciate that it exists. **What do you dislike about Google Cloud Deep Learning VM Image?** The costs are not a flaw of the image, but they are the context you live in. The image is free and the A100 or H100 attached to it very much is not, and because everything works immediately, it is easy to leave a GPU instance idle overnight out of habit. I schedule automatic shutdowns and lean on Spot instances for anything interruptible, and after adopting both the bill stopped surprising me. New users should set that discipline up on day one, because the platform will happily bill an idle accelerator at full rate. GPU quota is the friction I hit most often in practice. A fresh project starts with a GPU quota of zero in most regions, so the first launch attempt fails with a quota error and you file an increase request and wait. Approval has usually been quick for me, but it is an unadvertised speed bump directly in front of a product whose selling point is speed, and it confuses people the first time. My workaround is boring: request quota in two or three regions ahead of need, so capacity or approval delays in one region do not stall a deadline. The framework lineup has narrowed over time. The current image families center on PyTorch and the generic base and HPC images, and older framework variants age out of the update cadence rather than being maintained indefinitely. That is a defensible engineering choice, and it matches where the ecosystem went, but if your stack depends on an older framework build you will find yourself pinning an aging image family and inheriting its stale packages. Reading the release notes before committing a long-lived project to a specific family is worth the ten minutes. Documentation is serviceable rather than good. The core pages on choosing and creating an image are fine, but the material around the edges, upgrading in place, the exact contents of a given image version, what changed between releases, requires more digging than it should. More than once I have answered a question by SSHing into the instance and checking package versions directly instead of finding it written down. The image family naming convention helps, but a per-image manifest published somewhere obvious would help more. Last, small but real: the preinstalled environment is opinionated, and if your project needs different versions of the bundled libraries you end up creating a separate conda environment anyway, at which point some of the preinstallation is dead weight. The base image is the escape hatch here, and once I understood that split, picking the right variant per project made the issue mostly disappear. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Standing up a training machine meant creating a bare VM, installing the NVIDIA driver, matching a CUDA toolkit to it, matching cuDNN to the toolkit, matching the framework wheel to all three, and then discovering some pair disagreed and starting the forensic work. On a good day that was an afternoon. On a bad day it was two, and the second day was the one where I questioned my career. The Deep Learning VM collapses that entire sequence into a form on the Marketplace page. The benefit is not just the hours back, it is that the hours come back at the start of a project, exactly when momentum is worth the most. Reproducibility across a team is the second problem it quietly solves. Before, every person's training environment drifted from everyone else's, and works on my machine arguments consumed real meeting time. Now the launch script names an image family and a version, and every instance anyone creates from it is identical down to the driver. When a result does not replicate, the environment is off the suspect list immediately, which shortens every one of those investigations. Scaling experiments up and down stopped being an infrastructure event. The old pattern was to size a machine for the biggest job you expected and live with the waste the rest of the time, because reconfiguring was painful enough to avoid. With the image, an environment exists on whatever machine shape I launch, so a cheap CPU instance handles the data preparation, a single-GPU machine handles the debugging runs, and the multi-GPU machine exists only for the hours of the real training job. The environment is no longer welded to a specific box, and the spend follows the actual workload instead of the worst case. It changed how I treat short-lived experiments. When creating a working environment cost half a day, I hoarded configured machines and hesitated before trying anything that needed a different setup. When it costs five minutes, instances become disposable. I spin one up to test a hypothesis, keep the results in a bucket and the code in git, and delete the machine without ceremony. More ideas get tested because the price of testing one collapsed, and that shows up in the quality of what eventually ships. Onboarding collaborators went from a document to a command. The before-state was a setup guide that was outdated within a month and a new team member burning their first days fighting it. Now they run the launch script, wait for the boot, and open JupyterLab on an environment identical to mine. First useful contribution moved from the end of week one to the first afternoon, and I no longer maintain the setup guide at all. Hardware evaluation became something I actually do instead of something I estimate. Choosing between a T4, an L4, and an A100 for a given model used to be guesswork informed by spec sheets, because benchmarking each option meant building each environment. Now the same image boots on any of them, so I run the identical training script on two or three accelerator types for an hour each and let the throughput per euro decide. Several times the cheaper card won, and I would never have found that out under the old setup cost, because nobody benchmarks options that take a day each to prepare. The notebook-on-real-hardware workflow closed a gap I used to paper over. Prototyping locally on a laptop meant the code met the actual GPU, the actual CUDA version, and the actual data path only at training time, which is the worst moment to find a problem. With JupyterLab preconfigured on the training instance itself, exploration happens on the same stack the long runs use, and the class of bugs that only appeared in the transition from laptop to cloud simply stopped appearing. Prototype and production environment are the same machine now, and a whole category of surprises went away with the difference. ### 18. GPU-Ready in Minutes, But Costs and Compatibility Can Be Challenging **Rating:** 3.0/5.0 stars **Reviewed by:** Sahil T. | Senior Integration Consultant, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 04, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** The best thing about Google Cloud Deep Learning VM Image is its pre-configured, GPU-ready environment that lets us start training and experimenting with machine learning models within minutes, eliminating the complexity of setting up dependencies manually. **What do you dislike about Google Cloud Deep Learning VM Image?** The main drawback is the relatively high cost of GPU instances for long-running workloads. While the environment is pre-configured, upgrading frameworks or maintaining compatibility between CUDA, drivers, and ML libraries can occasionally require additional effort. New users may also find the initial setup and cloud networking concepts somewhat complex. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Google Cloud Deep Learning VM Image eliminates the hassle of manually configuring ML frameworks, CUDA, and GPU drivers. It enables us to provision a ready-to-use environment in minutes, speeding up model development and experimentation while reducing setup errors. This improves productivity, shortens project timelines, and lets the team focus on developing AI models instead of managing infrastructure. ### 19. A Pragmatic Review of Accelerators, Friction Points, and Real-World Utility **Rating:** 5.0/5.0 stars **Reviewed by:** Venkatesh d. | DevOps, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 29, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** Out-of-the-Box GPU/TPU Support: It comes pre-configured with optimized NVIDIA CUDA and cuDNN drivers, along with hardware-specific settings, so everything works right away without tedious compatibility checks. **What do you dislike about Google Cloud Deep Learning VM Image?** Bloat and unused packages: It includes numerous pre-installed libraries and tools that I may never use, which take up extra storage space and unnecessarily increase the attack surface. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Accelerates experimentation cycles by bypassing installation wait times for core libraries like PyTorch, TensorFlow, and Pandas, which helps speed up the transition from concept to rapid prototyping. ### 20. Easy Way to Get Started with GPU Workloads **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 28, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** What I like most is how quickly I can set up a GPU-based deep learning environment. The required drivers, frameworks, and common ML tools are already available, which saves a lot of time on manual installation and dependency issues. It also integrates well with Google Cloud GPU instances and provides enough flexibility to customize the environment as needed. **What do you dislike about Google Cloud Deep Learning VM Image?** One thing I’ve noticed is that package compatibility can sometimes be a little tricky. For example, when working with specific versions of PyTorch, TensorFlow, or CUDA, I’ve had to manually update or adjust packages to get everything working correctly. GPU VM costs can also add up quickly for long-running workloads. Better compatibility guidance between CUDA, drivers, and ML frameworks would be helpful. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** We previously spent quite a bit of time setting up GPU drivers, CUDA, PyTorch, and TensorFlow manually. The Deep Learning VM Image gives us a ready-to-use environment, so we can start testing and running ML workloads much faster. It reduces setup and dependency issues and makes it easier to create consistent environments for different projects. ### 21. Powerful for Scalable ML, but a Steeper Learning Curve **Rating:** 3.5/5.0 stars **Reviewed by:** Antony P. | Cloud enginering Manger, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 07, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** I like the tensorflow because it makes it easier to develop and deploy machine learning models at scale. specially appreciate the integration with kereas which makes everything easier **What do you dislike about Google Cloud Deep Learning VM Image?** It’s not that I dislike it, but one challenge is that it has a steeper learning curve compared to other frameworks, especially when it comes to advanced features. With Keras, some common tasks can also feel less intuitive at times. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** It helped me with the environment setup and configuration for machine learning projects. Normally, setting up TensorFlow or PyTorch can take a significant amount of time and may lead to compatibility issues, but the Deep Learning VM comes preconfigured with these components, which lets me start developing and training models immediately. ### 22. A convenient starting point for deep learning projects **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Higher Education | Enterprise (> 1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 12, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** What I like most is that the VM comes with many of the tools and frameworks needed for deep learning already set up. It saves a lot of time that would otherwise go into configuring Python, CUDA, GPU drivers, and ML libraries. I found it particularly useful for getting a training environment running quickly and then focusing on the actual model rather than troubleshooting the setup. **What do you dislike about Google Cloud Deep Learning VM Image?** The main downside is that the preconfigured environment can still take some time to understand if you are not already familiar with Google Cloud. I also found that package and framework versions can sometimes need adjustment depending on the project, so I still had to do some environment troubleshooting. The setup saves time overall, but it does not completely remove configuration work. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** The main problem it solves for me is the time and effort involved in setting up a reliable environment for deep learning experiments. Instead of configuring the GPU, drivers, CUDA and ML libraries from scratch every time, I can start with an environment that is already prepared for this type of work. This helps me get to model training and testing faster and makes it easier to reproduce experiments when working on different projects. ### 23. GPU-Powered Google Virtual Environment for Data Science and Machine Learning **Rating:** 4.0/5.0 stars **Reviewed by:** Kyle P. | Frontend Developer (Unpaid), Computer Software, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** August 04, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** I am able to use google virtual environment to be able to do data science and machine learning. This uses GPU to be able to run the virtual machine with the machine learning and data science algorithms when building. **What do you dislike about Google Cloud Deep Learning VM Image?** You are limited with the amount of GPU and CPU usage for the virtual environments that are being used. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** When working with data science and machine learning and AI you can use the VM images to be able to run the necessary programs and LLMs in the virtual environment wiht the use of the CPU or GPU. The google cloud deep learning VM image come with key ML frameworks and tools pre-installed which makes it easier to. ### 24. Excellent Preconfigured Environment for Fast AI/ML Development on Google Cloud **Rating:** 4.0/5.0 stars **Reviewed by:** Aditya G. | Sales Account Manager, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** May 27, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** What I like best about Google Cloud Deep Learning VM Images is how quickly they let you deploy a fully configured AI/ML environment without spending hours on setup and dependency management. Key advantages: Pre-installed frameworks like TensorFlow, PyTorch, JupyterLab, CUDA, and NVIDIA drivers GPU-ready environments optimized for AI workloads Faster experimentation and model training **What do you dislike about Google Cloud Deep Learning VM Image?** One challenge with Google Cloud Deep Learning VM Images is managing costs and optimizing resources, especially when running long workloads on high-end GPU instances. There are a few other limitations as well. There can be an initial learning curve for users who aren’t familiar with Google Cloud networking and IAM. GPU availability may also be limited in certain regions at times, which can make it harder to get the exact instance you need. Finally, the preconfigured environments sometimes include packages or versions that don’t align perfectly with specific project requirements, so some adjustments may be necessary. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Benefits include: Faster deployment of AI/ML projects with minimal setup time Reduced infrastructure and configuration overhead for engineering teams Improved productivity for developers and data scientists Easier access to GPU-powered computing for model training and inference ### 25. Easy to Use, Seamless Integration, and Great Value **Rating:** 5.0/5.0 stars **Reviewed by:** Jonathan M. | IT Tech, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 31, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** I absolutely love the program and how it works. It’s easy to use and very easy to integrate. Its performance really helps during meetings, and the overall price is good. I would definitely recommend it. **What do you dislike about Google Cloud Deep Learning VM Image?** I honestly don’t have anything negative to say or any dislikes regarding the program. It works great, and it’s user-friendly. Customer service is always willing to help and is very helpful whenever I need it. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Like I mentioned on the previous slide, I enjoyed working with it. I don’t have much else to say besides that it’s a very helpful tool to have. ### 26. Easy Setup with Pre-Installed Deep Learning and Smooth Google Cloud Integration **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Accounting | Small-Business (50 or fewer emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 27, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** I like how easy it is to set up and start using. The pre-installed deep learning frameworks, GPU support, and integration with Google Cloud save a lot of configuration time and make it convenient to run machine learning workloads. **What do you dislike about Google Cloud Deep Learning VM Image?** The main drawback is that the setup can still feel complex for beginners, especially when choosing the right VM, GPU, drivers, and framework versions. Costs can also increase quickly if high-performance instances are left running longer than needed. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Google Cloud Deep Learning VM Image removes much of the manual work involved in setting up a machine learning environment. It provides pre-configured frameworks, drivers, and GPU support, which reduces setup time and compatibility issues. This allows me to focus more on developing, testing, and running machine learning models rather than managing infrastructure. ### 27. A Convenient Way to Get an ML Environment Running Quickly **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 04, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** The biggest advantage is that it removes most of the setup work. Instead of spending time installing frameworks, drivers, and dependencies, you can start working with a ready-to-use environment. It already includes the common machine learning libraries, which makes it much easier to experiment with models or run training jobs without worrying about configuration. **What do you dislike about Google Cloud Deep Learning VM Image?** The initial experience is straightforward, but managing costs requires attention if you leave VM instances running. Also, if you need very specific library versions or custom environments, you'll still end up doing some manual configuration. Better guidance for version compatibility would be useful. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** It saves the time that would normally be spent preparing a machine learning environment from scratch. Instead of troubleshooting installations, I can focus on building and testing models. This has made it easier to prototype ideas and get new projects started much faster. ### 28. Saves Setup Time with Google Cloud Deep Learning VM Image **Rating:** 5.0/5.0 stars **Reviewed by:** Vivek C. | Team Manager, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 08, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** The best part about Google Cloud Deep Learning VM Images is how much time they save on environment setup—you get a stable, pre-configured stack right out of the box. Having popular frameworks like PyTorch and TensorFlow ready to go, alongside JupyterLab, makes it incredibly easy to jump straight into training models without getting bogged down in installation and configuration. **What do you dislike about Google Cloud Deep Learning VM Image?** It is highly convenient for starting projects, but custom dependency management and zone resource limits require careful planning. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Saves days of engineering frustration and prevents broken development environments. ### 29. Flexible, Scalable, and Easy to Standardize Images Across Workflows **Rating:** 5.0/5.0 stars **Reviewed by:** Abhishek H. | Cloud Analyst, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 06, 2026 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about Google Cloud Deep Learning VM Image?** Its very flexible and scalable My team can standardise the version of image and easily reproduce them across different workflows The already installed framework and libraries help get better results **What do you dislike about Google Cloud Deep Learning VM Image?** Nothing in particular so far The image bloat causes delay usually **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Environmental Breakdown- team members can work on their local and push to production The inbuilt packages and libraries help the team to get faster output Benefits are Faster Go live to market Reduces Secured and Governance ### 30. Speeds Up AI/ML Setup with Tensor and Python Support, Plus Cloud GPU **Rating:** 4.5/5.0 stars **Reviewed by:** Soumya K. | System Analyst, Broadcast Media, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** August 07, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** It supports the predefined AI and ML frameworks like Tensor, Python. It reduces the time for manual setup and Dependency. Also it offers cloud services and GPU. **What do you dislike about Google Cloud Deep Learning VM Image?** It is overwhelming for beginners with large number of tools. Use of GPU instances can be expensive. Troubleshooting can be difficult for users at times. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Solves the issue for time required to do the initial set up. It comes with predefined tools which solves the compatibility issues. ### 31. Easy Setup with Everything Needed to Start ML Projects Fast **Rating:** 4.5/5.0 stars **Reviewed by:** Adya S. | Student, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 22, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** I like that it’s easy to set up and comes with the tools and drivers I need, so I can start working on ML projects quickly. **What do you dislike about Google Cloud Deep Learning VM Image?** The setup can be a bit costly, and sometimes updates or configuration can be confusing. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** It saves me a lot of setup time and makes it easier to get ML models up and running quickly. ### 32. Personalized Learning Insights, But Not Fully Automatic **Rating:** 3.5/5.0 stars **Reviewed by:** Christina w. | Certified Nursing Assistant, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 04, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** The data it collects is stored to reflect each user and train them the way they learn as opposed to training the class as a whole **What do you dislike about Google Cloud Deep Learning VM Image?** It is not automatic and has to be opened like every other progran **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** It trains me and my coworkers on our own individual learning style instead of all of us on the same level ### 33. Plug-and-Play Deep Learning VM Image, but Offline Reliability Needs Work **Rating:** 3.5/5.0 stars **Reviewed by:** Benjamin G. | Ammunition Specialist, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** July 07, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** Google Cloud Deep Learning VM Image works great, no headache setup. Plug and Play. **What do you dislike about Google Cloud Deep Learning VM Image?** Doesn't work well offline and shutsdown quite often. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Offline accessibility and faster GPU upgrades. ### 34. Clean UI and Easy Image Scanning, but Integrations and Latency Need Work **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Higher Education | Small-Business (50 or fewer emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 13, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** The UI is clean, all necessary things are on the dashboard. I did one project for Car Plate Number Detection. The image scanning code was easy to implement and run. **What do you dislike about Google Cloud Deep Learning VM Image?** The integrations and latency is not that good. I would be able to do fast output if those were top notch. ROI is not up to the mark. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** I am doing projects on my own for academic or just for output based learning. ### 35. Powerful tool to help modernise your business **Rating:** 4.5/5.0 stars **Reviewed by:** Ryan C. | Operations manager, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** July 18, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** We are able to process our organisation key performance data much more quickly now **What do you dislike about Google Cloud Deep Learning VM Image?** It may be a free start but it soon becomes very costly. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** With it being open to developers we have been able to get assistance to get the user interface more suited to us ### 36. Time-Saving Tool with Key Machine Learning Frameworks **Rating:** 5.0/5.0 stars **Reviewed by:** Shilo J. | Article Writer, Education Management, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 19, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** how it saves time. It comes with key machine learning frameworks **What do you dislike about Google Cloud Deep Learning VM Image?** Some images come out distorted coz of internet **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Built-in JupyterLab support lets you start coding right away. ### 37. Fully Configured GPU ML Environment That Gets You Training Fast **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Enterprise (> 1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 11, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** It gives a fully configured GPU ready ML environment, with frameworks such as TensorFlow and PyTorch plus all drivers installed. This removes setup issues & helps start training models asap **What do you dislike about Google Cloud Deep Learning VM Image?** nothing that I can think of as of now, will know more later **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Eliminates manual setup of drivers and ML frameworks by providing a configured, GPU‑ready VM. Saving time, avoid compatibility issues and start training models asap. ### 38. All-in-One AI Environment with TensorFlow and Easy NVIDIA GPU Setup **Rating:** 4.0/5.0 stars **Reviewed by:** arnab s. | Technical Support Specialist, Information Technology and Services, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** July 31, 2026 **What do you like best about Google Cloud Deep Learning VM Image?** all in ai enviroment tensorflow and easily attach GPU's Nvidia **What do you dislike about Google Cloud Deep Learning VM Image?** gpu cost a bit high and always cloud dependency **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** ai model training makes easy including deep learning and for academic research ### 39. Google Cloud Deep Learning VM Image Review **Rating:** 5.0/5.0 stars **Reviewed by:** Ramcharn H. | Senior Customer Service Officer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** October 19, 2023 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about Google Cloud Deep Learning VM Image?** You can quickly provision a VM with everything you need for your deep learning project on Google Cloud. Deep Learning VM Image makes it simple and quick to create a VM image containing all the most popular. **What do you dislike about Google Cloud Deep Learning VM Image?** I found documentation to be a bit confusing as to where to search for reference and examples **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Google after being the leading search engine provider dived into the cloud frenzy and it didn't disappoint. Its pricing is competitive , it has most of the features that other cloud providers have. ### 40. I developed and trained various deep learning models. **Rating:** 4.5/5.0 stars **Reviewed by:** Ali Burak . | Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **AI Translated:** This review has been translated from Turkish using AI. **Reviewed Date:** October 28, 2023 **What do you like best about Google Cloud Deep Learning VM Image?** It is easy to set up and use. Thanks to its performance and scalability, I can train larger and more complex models. **What do you dislike about Google Cloud Deep Learning VM Image?** The cost is very high, it's easy to use, but there aren't many options to create a more customized environment for myself. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** I used it to develop a natural language processing model in a project. I introduced speech recognition models for translation. ### 41. Most recommended tool for Deep Learning **Rating:** 5.0/5.0 stars **Reviewed by:** Prashant P. | Business Development Manager, Information Technology and Services, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** October 18, 2023 **What do you like best about Google Cloud Deep Learning VM Image?** The Google Cloud Deep Learning is really a market game changer product in deep learning. Its really very easy to use and has good performance an security. It is cost efficient. **What do you dislike about Google Cloud Deep Learning VM Image?** There is nothing that can be disliked. Google Cloud Deep Learning VM Image is awesome. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** It has benefited me in the documentation. It has preconfigured environments that saved my time. I can easily scale up and down as per my requirements. Easy for team collaboration. Overall it has saved my time a lot. ### 42. A good investment DL VM **Rating:** 4.0/5.0 stars **Reviewed by:** Ramakant S. | Jr. Data Scientist, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** November 02, 2023 **What do you like best about Google Cloud Deep Learning VM Image?** Nice tool for data scientists/ML engineers. It has all the latest modules and is easy to install. Pre-installed common dependencies and driver support for GPU/TPU and CLI options make it awesome. It makes possible a few hours of work done in a few clicks. **What do you dislike about Google Cloud Deep Learning VM Image?** Nothing major as of now. faced an issue while restarting of collab notebook instance. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** It saves hours of debugging, makes faster deployment, and is easy to use. ### 43. Great Cloud Platform **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Staffing and Recruiting | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** October 18, 2023 **What do you like best about Google Cloud Deep Learning VM Image?** It's the best tool I have used because of easy access and the User Interface is very nice, anyone can use it easily without having any training or knowledge about it. **What do you dislike about Google Cloud Deep Learning VM Image?** I didn't face anything in this tool which I would say that this is not good because I have never gone through with those things. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** It's a great AI framework. It makes it easy and fast to exemplify a VM image and the best part is we can also easily add cloud GPU and cloud TPU and also it supports the latest machine learning framework which is a great thing. ### 44. It has the tools that you want, the best combination ever **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Small-Business (50 or fewer emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through Google using a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** October 17, 2023 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about Google Cloud Deep Learning VM Image?** The Deep Learning VM Images come pre-installed with popular machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. This is a significant time-saver as setting up these environments can be complex and time-consuming. **What do you dislike about Google Cloud Deep Learning VM Image?** While you only pay for the resources you use, Google Cloud services can become expensive, especially when using powerful GPUs or TPUs. This may not be cost-effective for small businesses or individual developers. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Setting up a deep learning environment from scratch can be complex and time-consuming. It involves installing the operating system, configuring drivers, and installing multiple libraries and dependencies. ### 45. Best image for Deep learning Project **Rating:** 5.0/5.0 stars **Reviewed by:** Sankalp A. | Research Associate, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** October 20, 2023 **What do you like best about Google Cloud Deep Learning VM Image?** Easy and Fast implementation of deep learning project on google cloud. It comes with pre-installed required softwares like PyTorch, Scikit-learn, Tensorflow and many more. **What do you dislike about Google Cloud Deep Learning VM Image?** Nothing to dislike about Google cloud deep learning vm image. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Created deep learning project with the help of google cloud vm image without worrying about initial setup. ### 46. A deep learning virtual machine on Google Cloud and Effortless Deep Learning Setup . **Rating:** 5.0/5.0 stars **Reviewed by:** prajakta k. | DBA, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** October 20, 2023 **What do you like best about Google Cloud Deep Learning VM Image?** Google Cloud is easy to use.the best support for the customer. ease implementation. and we use this daily. This Google Cloud has so many features. **What do you dislike about Google Cloud Deep Learning VM Image?** There is no such dislike for Google Cloud. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** This helps me save time. and reducing technical issues with software installation. faster AI model development and experimentation. ### 47. Configurable and extensible but has a learning curve **Rating:** 3.0/5.0 stars **Reviewed by:** Daniel O. | TIP Contributor, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** October 19, 2023 **What do you like best about Google Cloud Deep Learning VM Image?** It has a lot of nobs & configurations to adapt to the user's needs regardless of how simple or how complex. **What do you dislike about Google Cloud Deep Learning VM Image?** The learning curve to use the software is quite steep. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Managing the installation and configuration of ML libraries as well as built-in GCP integration. ### 48. Google Cloud Deep Learning VM Image Review **Rating:** 4.0/5.0 stars **Reviewed by:** Udit S. | Professional 2:-Application Delivery, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** October 17, 2023 **What do you like best about Google Cloud Deep Learning VM Image?** It helps in building Deep Learning projects very fast on Google Cloud Platform. It's amazing. It has broad support, fast prototyping features which are good. **What do you dislike about Google Cloud Deep Learning VM Image?** The user interface of the platform can be improved. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** Deep Learning projects, prototyping, accessibility are the features which are beneficial. ### 49. Google cloud deep learning review **Rating:** 3.5/5.0 stars **Reviewed by:** hanne d. | learning builder, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** October 18, 2023 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about Google Cloud Deep Learning VM Image?** This app corresponds with other Google applications. **What do you dislike about Google Cloud Deep Learning VM Image?** There is a learning curve to get to know this. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** This app corresponds with other Google apps which makes working fast. ### 50. Good product for user interface & ease **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Staffing and Recruiting | Small-Business (50 or fewer emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through a business email account **Source: Organic:** Organic review. This review was written entirely without invitation or incentive from G2, a seller, or an affiliate. **Reviewed Date:** November 07, 2023 **What do you like best about Google Cloud Deep Learning VM Image?** There is not a ton of setup or learning curve with this - its nice to use immediately. **What do you dislike about Google Cloud Deep Learning VM Image?** I didn't use everything this product is intended for and therefore did not run into any problems. **What problems is Google Cloud Deep Learning VM Image solving and how is that benefiting you?** We used it since it had many things come pre-installed and would be fast & easy. - [View Google Cloud Deep Learning VM Image pricing details and edition comparison](https://www.g2.com/products/google-cloud-deep-learning-vm-image/reviews?section=pricing&secure%5Bexpires_at%5D=2026-09-14+14%3A44%3A58+-0500&secure%5Bsession_id%5D=88c0ae99-28c9-4d6f-97b5-7eb5b973e8c2&secure%5Btoken%5D=435a1a933f9b6847729fd6bc7df0f97679c7f4549da9f072bf1a5220219a15cb&format=llm_user) ## Google Cloud Deep Learning VM Image Features **Additional Functionality** - Tagging - Natural Language Processing - Data Extraction - Multi-Language - Predictive Analytics - Drag & Drop - Speech Recognition - Reporting/Analytics - Data Storage Management - Virtual Personal Assistant (VPA) - AI Copilot - Customer Segmentation - Collaboration Tools - Data Import/Export - Generative AI - For eCommerce - Role-Based Permissions - Customizable Branding - Search/Filter - Monitoring - Document Management - API - Data Visualization - Trend Analysis - Machine Learning - Access Controls/Permissions - Alerts/Escalation - Performance Metrics - Real-Time Data - Third-Party Integrations - Mobile App - Multiple Data Sources - For Sales Teams/Organizations - Sentiment Analysis - Activity Dashboard - Chatbot - Workflow Automation **Additional Functionality** - No-Code - Search/Filter - Data Import/Export - Data Visualization - Activity Dashboard - Workflow Management - Quality Control - Third-Party Integrations - Model Training - Multi-Language - Data Profiling - Visualization - Data Capture and Transfer - Predictive Analytics - Performance Metrics - Image Recognition - Version Control - Neural Network Modeling - Generative AI - Synchronous Learning - Natural Language Processing - Monitoring - Data Transformation - Customizable Reports - Voice Recognition - Performance Management - Compliance Management - Troubleshooting - Content Management - Asynchronous Learning - Real-Time Reporting - API - Artificial Neural Networks - AI Copilot - ML Algorithm Library - Real-Time Data - Collaboration Tools - Self-paced Learning - Data Extraction - Configuration Management - Data Security - Visual Analytics - Activity Tracking - Continuous Delivery - Document Classification - Image Analysis - Progress Tracking - Reporting & Statistics - Real-Time Monitoring **Core Functionality - Artificial Neural Network** - Neural Network Training - Neural Network Testing - Model Evaluation - Compliance **Data Handling - Artificial Neural Network** - Data Integration - Data Preprocessing **Performance - Artificial Neural Network** - Model Optimization - Scalability **Usability - Artificial Neural Network** - User Interface - Documentation & Support - Customizability **Advanced Features - Artificial Neural Network** - Deep Learning Capabilities - Transfer Learning - Real-Time Processing - Automated Model Tuning - Visualization Tools **Agentic AI - Artificial Neural Network** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making ## Top Google Cloud Deep Learning VM Image Alternatives - [Keras](https://www.g2.com/products/keras/reviews) - 4.6/5.0 (64 reviews) - [AIToolbox](https://www.g2.com/products/aitoolbox/reviews) - 4.4/5.0 (35 reviews) - [Microsoft Cognitive Toolkit (Formerly CNTK)](https://www.g2.com/products/microsoft-cognitive-toolkit-formerly-cntk/reviews) - 4.2/5.0 (22 reviews)