G2

Shiv K.

Maintenance Engineer

Manufacturing

Mid-Market (51-1000 emp.)

8/29/2026

"Good automation for faster machine learning workflows"

4.5/5

Describe the project or task Google Cloud AutoML helped with:

Google Cloud AutoML offers a suite of machine learning products that enable developers with limited ML expertise to train high-quality models specific to their business needs. By leveraging Google's state-of-the-art transfer learning and neural architecture search technologies, AutoML simplifies the process of building custom models. It provides a user-friendly interface for data preparation, model training, and evaluation, making it accessible to a broader audience. The service integrates seamlessly with other Google Cloud services, ensuring a cohesive workflow from data ingestion to model deployment. Review collected by and hosted on G2.com.

What do you like best about Google Cloud AutoML?

What I like most about Google Cloud AutoML is how it simplifies model training without requiring a lot of manual coding. The UI is straightforward, and its integration with other Google Cloud services makes it easier to manage data and deploy models. Training performance has been solid for quickly testing different models, and the automation saves time throughout development. It can deliver good ROI for teams that want to build ML solutions faster, although pricing still depends on usage. Onboarding and the Google Cloud documentation are helpful, and for my workflow the biggest advantages are the automated model selection and training. Review collected by and hosted on G2.com.

What do you dislike about Google Cloud AutoML?

What I dislike about Google Cloud AutoML is that parts of the UI can feel confusing when you’re setting up models for the first time. The integrations are useful, but configuring data sources and permissions can still take a while and isn’t always as straightforward as I’d like. Training costs can also climb quickly with larger datasets, which can impact ROI. Overall performance is generally good, but training times can vary depending on the workload and how much data is involved. Better onboarding, clearer pricing estimates up front, and more step-by-step guidance during model setup would make the whole experience easier and less time-consuming. Review collected by and hosted on G2.com.

Recommendations to others considering Google Cloud AutoML:

To improve the experience with Google Cloud AutoML, I recommend enhancing the UI to make it more intuitive, especially for first-time users. Providing clearer guidance on setting up data sources and permissions would be beneficial. Additionally, offering more transparent pricing information and detailed onboarding processes could help users better plan their projects. Finally, implementing more comprehensive step-by-step tutorials for model setup would greatly enhance user satisfaction. Review collected by and hosted on G2.com.

What problems is Google Cloud AutoML solving and how is that benefiting you?

Before we started using Google Cloud AutoML, we spent a lot of time manually preparing models, setting up the environment, testing different approaches, and managing experiments ourselves. AutoML has significantly reduced that workload by automating model training and making experimentation much easier through its straightforward UI. The integration with other Google Cloud services also helps keep the workflow smooth and connected, and the model performance has been solid for our use cases. Overall, it has helped us save development time and cut down on repetitive manual work, which has improved our ROI. The documentation and onboarding support have also made it simpler to get started and move forward with AI solutions. Review collected by and hosted on G2.com.