I primarily use Google Cloud for analytics purposes. The data originates from our on-premise cloud system but is transferred to Google Cloud for analysis.
Regarding Google Cloud's data analytics features, I find the presentation of information and the ability to move and transform data to be the most valuable. As for machine learning capabilities, they are helpful, especially in detecting anomalies or outliers in our data. For instance, Google Cloud can identify unusual network behavior and provide recommendations on how to address or leverage these findings effectively.
I think one area that could be improved in Google Cloud is the customer support. Sometimes the integration process can be a bit complex due to format errors. Also having clearer documentation or a larger support team could really help users like me understand and resolve issues more effectively.
I have been using Google Cloud for the past four years.
In terms of stability, I'd give Google Cloud a nine out of ten as it's very stable.
Google Cloud is scalable, which serves our organization's scaling needs well. We have multiple users directly interacting with the technology, and while we don't have immediate plans to increase usage, we would consider extending it in the future.
As for scalability, I'd rate it a ten out of ten as it's more than enough for our needs.
Integrating Google Cloud with our existing systems has been. Deployment is super fast, and takes a couple of minutes.
Google Cloud has helped us save time, approximately around thirty to forty percent.
Google Cloud is on the expensive side for us. I don't have exact figures on the cost per year or month.
I haven't used Google Cloud for AI projects personally. But I have utilized Google AI for optimizing Google Ads campaigns. It's an automated machine learning model that uses data from websites to understand customer behavior and improve marketing strategies for better results like increasing clicks or purchases.
My advice for those considering Google Cloud is to have a technical advisor due to its complexity. It's data-driven, stable, and scalable, which are strong advantages.
Overall, I would give it a rating of around an eight out of ten.