Google Cloud AI Platform offers robust AI services with features like handwritten text recognition and video classification, positioned as a cost-effective option for diverse industries.
| Product | Mindshare (%) |
|---|---|
| Google Cloud AI Platform | 3.1% |
| Gemini Enterprise Agent Platform | 8.0% |
| Azure OpenAI | 6.8% |
| Other | 82.1% |
| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 2 |
| Large Enterprise | 2 |
| Company Size | Count |
|---|---|
| Small Business | 62 |
| Midsize Enterprise | 35 |
| Large Enterprise | 99 |
Google Cloud AI Platform provides tools for customizable AI applications, enabling efficiency with its algorithms and solutions like BigQuery and Firebase. Users benefit from the Google Vision API for text extraction and algorithmic integration, enhancing operational efficiency and adaptability. Despite its strengths, it may require simplification in model creation and user interface enhancements compared to Microsoft's offerings. Clarity in pricing, direct database integration, and improved documentation on API and service costs are desired improvements.
What are the key features of Google Cloud AI Platform?Organizations utilize Google Cloud AI Platform in industries like government for tasks such as integrating handwritten data into Excel, analyzing PIA data, and creating custom applications. It supports the deployment of cloud applications and development of end-to-end pipelines, meeting specific client demands effectively.
Google Cloud AI Platform was previously known as Google Cloud for AI.
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| Author info | Rating | Review Summary |
|---|---|---|
| Owner at Go knowledge | 4.0 | I use Google Cloud AI Platform primarily for Firebase due to its valuable databases and BigQuery integration. Although model management and cost transparency could improve, I find it more cost-effective than Oracle APEX, offering significant savings. |
| Managing Director at sea-solutión | 4.0 | I use Google Cloud AI Platform primarily to extract text from images using the Google Vision API, which I find valuable. However, improvements in text extraction accuracy and adjustments to pricing would be beneficial for my needs. |
| CTO at Intelli AI LLC | 4.5 | I find Google Cloud AI Platform valuable for its wide range of algorithms and integrated solutions that enable quick operational deployment. However, I believe further improvement could include adding more AI algorithms to enhance its current offerings. |
| Project Manager at Wipro Limited | 4.0 | I work in IT and we use Google Cloud AI Platform for its cost-effectiveness compared to Salesforce. However, customization is challenging and improvements are needed for easier database linking. It's more suited for larger organizations than Salesforce. |
| Owner at Go knowledge | 3.5 | I use this solution for handwritten list recognition, achieving 90% accuracy, halving human intervention compared to alternatives. It's stable, but I'd like simpler custom model creation and direct list recognition for better user-friendliness. |
| Data Engineer at a educational organization with 201-500 employees | 4.0 | I appreciate Google Cloud AI Platform's handy interface and its integration with Vertex for pipeline creation. However, I find room for improvement in features like generative AI and direct module transfers between development and production environments. |
| Student at Politechnika Gdańska | 3.5 | I utilized Google Cloud AI Platform to predict client satisfaction, finding its video and object classification features valuable. However, I encountered some errors and felt performance and pricing needed improvement. Previously, I used Vertex AI for similar projects. |
| Data Scientist at NeuStar | 4.5 | I've used this GCP solution for a year to validate encrypted PIA data and client records for accuracy. It evolved into a machine learning model, leveraging external sources for validation, and I expose API services to clients. I rate it 9/10. |
| Architect of solutions at ASIC SA | 4.0 | I use Google Cloud AI Platform for government clients, valuing its vast services and AI architecture. Setup was straightforward for me but could be difficult for others. The price is competitive, and I rate it 8/10. |
The most valuable feature I find is Firebase. Within Firebase, there are a couple of databases. Also, within Google, there is BigQuery, which is very valuable. The feedback left about these tools was really helpful and informative for us.
The model management on Google Cloud AI Platform could be better. The APIs and the services are not fully described with their costs, which can be somewhat cryptic. The console itself could also be a little bit better, specifically the Google Cloud Console.
I have been using Google Cloud AI Platform for about two years now.
I rate the stability of the solution as very stable. I would give it a nine out of ten.
I would definitely rate the scalability of this tool as an eight.
The technical support from Google is not very fast. I think it is about a five out of ten even though they have courses online where I can learn a lot, if I really need support, I have to wait a very long time.
Neutral
I used Oracle APEX before Google Cloud AI Platform. Oracle APEX is a free tool, except for the Oracle database, which I can only use with it. To have more freedom, I chose Firebase and Google's solutions as it allows me to run it on a hosted server if I want to.
The initial setup is easy in some ways and a little bit difficult in others, especially how I set up the APIs. Paying for the APIs does not work that well.
I did the deployment myself in house.
I have seen measurable benefits from Google Cloud AI Platform. Compared to Oracle APEX, hosting with Oracle costs around 1,500 euros per month, while this solution costs about 50 euros a month if kept under 4,000 users. That is a significant difference.
For the most part, the pricing is perfect sinceit grows with the use of my app. In some cases, they could be more specific about the pricing, especially for some AI features.
I evaluated Oracle APEX before choosing Google Cloud AI Platform.
I have knowledge of it, and I do recommend Google Cloud AI Platform to other people. I would definitely rate the overall solution as an eight out of ten.
We use Google Cloud AI Platform to extract text from images, such as forms.
The platform's Google Vision API is particularly valuable. It helps with text extraction from images.
Improvements in text extraction accuracy and pricing adjustments would be helpful.
The solution is stable.
We have less than 100 Google Cloud AI Platform users in our organization. It is scalable.
The setup is relatively straightforward. It took me two days to understand the process, but subsequent setups took about half an hour.
I rate Google Cloud AI Platform an eight out of ten.
It's a host of use cases depending on, again, the the client requirement.
A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up with an operational solution really quick.
I think it's the it it also has has evolved quite a bit over the last few years, and Google Cloud folks have been getting, more and more services. But I think from a improvement standpoint, so maybe they can look at adding more algorithms, so adding more AI algorithms to their suite.
I have been using the solution for the last six to seven years.
It is quite a stable solution. I would rate it eight out of ten.
It is a scalable solution.
The technical support is very good.
Positive
The initial setup is straightforward. The deployment takes few minutes time. I would rate it nine out of ten.
The pricing is on the expensive side.
My recommendation would be that Google Cloud removes a lot of treasury from compared to not using it and doing it thread PeerOps, and so I did in terms of, you know, the automated training platform training and the deployment platform that it provides, it's works really well when the client requirements are, to deploy a solution real quick. So I understand the solution, do a bit of forward in your sense, and then you'll be able to realize the potential of it.
Overall i would rate it nine out of ten.
I work for an IT company, and we use the solution to build applications for different customers.
In comparison to Salesforce, the product is much cheaper. So it is generally preferred by our clients. Since they are using it, we end up using it as well.
Customizations are very difficult, and they take time. The product should provide better customization. It should provide some out-of-the-box features that can be used to create a field or to make changes in the data validation. Linking to the database should be crisper so that we can do it with simple drag-and-drop functionalities instead of coding.
I have been working with the solution since 2014.
The stability depends upon how we utilize the licenses. However, the product is pretty stable. I rate stability a seven or eight out of ten.
I rate the scalability an eight out of ten. The number of users depends on the customers. We make products and applications for different clients, and they buy the licenses.
Neutral
I have used Salesforce. Salesforce is much more in demand. Salesforce is suitable for small enterprises, while Google Cloud AI Platform is suitable for larger organizations.
The initial setup is very straightforward. We just need to log in and create our credentials, and it's easy to go. The initial setup takes just a couple of minutes.
The licenses are cheap. The licensing cost varies based on the client’s requirements, the number of licenses needed, use cases, and the features they require. I rate pricing an eight or nine out of ten.
I am using the latest version of the solution. We use private and public clouds based on the project we are working on. I am a project manager. I'm not a developer who is using the product end-to-end daily. Our clients buy the licenses and hand them over to us. We do some development or customizations they are looking for and hand them over the solution. I will recommend the solution to others. Overall, I rate the solution an eight out of ten.
The primary use case for this solution is to read handwritten lists and input them into Excel files. I also use the solution to read scannable material to determine if it is still usable.
I find the handwritten feature that recognizes handwriting text to be the most valuable feature.
The solution can be improved by simplifying the process to make your own models. Currently, I am using lower-end cameras and they can not recognize every object. The solution can take a page out of Microsoft's AI within Office 365 to make it more user-friendly. Allowing the solution to directly recognize lists so they don't have to be written out would make it more useful.
I have been using the solution for six months.
The solution is extremely stable.
I believe the solution is as scalable as Microsoft and Amazon.
The initial setup was straightforward.
The implementation was done in-house using the documentation included in the solution. It took about four hours to start using the solution.
The cost for the solution is based on the number of uses. For every thousand uses, it is about four and a half euros. For 21,000 lists that require processing, it will cost about one hundred euros.
I evaluated Microsoft's AI for Office 365, and Amazon Textract.
I rate this solution seven out of ten.
I found that Microsoft's AI was able to read 70% of the documents correctly with 30% incorrect. The solution is able to read 90% of the documents correctly with a 10% error rate. This cuts the human intervention time by more than half compared to Mircosoft's solution.
The solution does not require much maintenance, only about two days a month.
I would recommend anyone looking to use this solution first review the pricing rates peruse. There are a lot of free solutions on the market, but this one is very useful.
I like to develop inside platforms like Vertex and Google. I use such specific things to create and develop my skills.
Recently, I was using QFlow for generating the pipelines, and in the recent version, I originally worked with Google Cloud AI Platform, along with Vertex.
With Google Cloud AI Platform, I tried to create the end-to-end pipelines and adopt pipelines using Vertex for incorporating all the features from serving online endpoints along with past predictions.
I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms. Apart from that, I am a bit confused regarding what it provides apart from since we have alternatives like SageMaker or Azure ML. I need to know what I can achieve through it.
One thing that I found is that Azure ML does not directly provide you with features on Google Cloud AI Platform, whereas Vertex provides some features of the platform. I see room for improvement in the aforementioned area.
Currently, I think the generative AI isn't quite a boost, and Azure is working a lot with it, so I think Google should incorporate it by considering it as a bit of an eye-opener towards their own side. That can put them at a greater advantage compared to when they attempt to integrate themselves indirectly into the pipeline. They also provide PPU servers as well as their distributed server, which are not currently available in the pipeline. If they are able to incorporate the aforementioned details for improvement, then they will be able to lead.
One more thing that you can like is when you have a production environment and development environment, and I want to push my modules from the development environment to the production environment, it is not currently possible in Google Cloud AI Platform. If I have two projects where one is the development and the other one is the deployment, and if I try to pass my modules from the module registry, it is not possible right now. That is where it lacks. I sent a request to the support team regarding an issue that we were trying to pass to the development project and how it is not possible yet.
I have been using Google Cloud AI Platform for the past eight to nine months. Also, I am using the solution's latest version. I am working with the SaaS version of the solution.
There are no issues with the solution's stability.
It is scalable in the deployment stage, but I just created it recently, so I cannot speak much on scalability.
Currently, we are looking forward to how it works. Then, if it's widely beneficial for us in the future, we'll integrate it. But currently, we are just researching it in our company.
I am the only person who is testing the solution.
The technical support has been good. It's much better. They provide us with a response in approximately an hour and a half.
The initial setup is quite easy. I'm very familiar with it.
For the deployment process, I just create and push the module into the module since we need to create the endpoint for the same. And along with the container for prediction, I deployed the module to the endpoint that I created.
Endpoint creation takes a bit of time, but deployment takes a lot of time. It takes quite a long if you are also connecting the process container along with it for protection.
Originally, it also provided us with all the services, like, all machinery or replicas to create. I directly use those. I did the deployment by myself, and there were additional machines just involved.
Currently, whatever Google Cloud AI Platform has, the same thing can be done with SageMaker or Azure ML. There is not much of a difference, but I found out that using QFlow in Google Cloud AI Platform is quite easy compared to another platform. At the same scale, you can achieve it on other platforms too.
I have recently deployed it, so I cannot explain much about the maintenance part. But, of course, they provide a feature store so that it can be handy in the future.
Currently, I think it is easy to use. And if someone wants to create an end-to-end pipeline and they are new to the platform, then Google Cloud AI Platform would be much better.
I rate the overall solution an eight out of ten.
I used Google Cloud AI Platform for a project at AltaML, where I had to predict client satisfaction levels for a particular company. At that time, I didn't have experience with APIs or anything like that, so my experience with the platform was limited. I think Azure has more options for me since they have a design feature that Google doesn't have.
I think that the Google Cloud AI Platform has some really great options, including the ability to work with video and classify objects within it. I was particularly interested in how the platform functions and its capabilities in this area.
It could be more clear, and sometimes there are errors that I don't quite understand. So maybe they could improve the pricing. The performance could also be better.
I used the latest version of Google Cloud AI Platform for a short period and was a user of the solution.
It is really hard for me to say anything about its stability because I don't have that much experience with it.
I was the only user in my company using the solution.
I have had an experience with the technical support team, and they replied to all my queries.
Since the model could be trained in just a couple of hours and deploying it took only a few minutes, the entire process took less than an hour. Additionally, the same procedure was applicable to Microsoft.
The solution has an attractive starting program, which costs only 300 USD for a duration of three months. During this period, one can accomplish a lot of work on the solution.
I can definitely recommend the solution to those planning to use it. Overall, I would rate the solution a six to seven out of ten.
We are working on PIA data, which is in encrypted format. We get a set of records for each individual. This record size could be 10 to 50 for each individual. We need to identify the best email and phone number out of these set of records. We are developing a sense data model for that and along with the PIA attributes for each individual, we also take help from different sources – like AT&T and T-Mobile – that are going to provide telecom services. We take data from these providers for validation purposes.
We don't consume their data, but we use their data for validation. We bounce our data into their records and they say, "This this is correct," or "This is not correct."
On GCP, we are exposing our API services to our clients so that they send us their information. It can be single individual records or it can be a batch of their clients.
The clients, in a sense, want to validate their data if there could be chances that it's stale in nature. They want to make sure that their records of individuals are up-to-date.
We are using the latest version on the public cloud and deploying it on GCP.
We are trying our best to improve our existing models and privacy and to keep on updating it, and also we are trying to use reinforcement learning and separate APIs so that if a user wants to update their data, they can do so.
At first, there were only the user-managed rules to identify the best attributes of the individual. Then, we came up with a truth set and developed different machine learning models with the help of that truth set, so now it's completely machine learning.
I have been using this solution for a year.
We tried multiple solutions before we finalized our machine learning model.
We have another team that takes care of data cleansing and deployment.
I would rate this solution 9 out of 10.
In the past, I have deployed the solution for government clients wanting cloud applications and databases services.
Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture.
I have been using the solution for three years.
The initial setup was straightforward for me but could be difficult for others.
The price of the solution is competitive.
I rate Google Cloud AI Platform an eight out of ten.
