Google Cloud AI Platform vs Microsoft Azure Machine Learning Studio comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Mar 6, 2024
 

Categories and Ranking

Google Cloud AI Platform
Ranking in AI Development Platforms
7th
Average Rating
7.8
Number of Reviews
7
Ranking in other categories
No ranking in other categories
Microsoft Azure Machine Lea...
Ranking in AI Development Platforms
1st
Average Rating
7.8
Number of Reviews
57
Ranking in other categories
Data Science Platforms (2nd)
 

Mindshare comparison

As of July 2024, in the AI Development Platforms category, the mindshare of Google Cloud AI Platform is 5.7%, up from 5.5% compared to the previous year. The mindshare of Microsoft Azure Machine Learning Studio is 15.9%, up from 14.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
Unique Categories:
No other categories found
Data Science Platforms
5.7%
 

Featured Reviews

Vipul-Kumar - PeerSpot reviewer
Nov 3, 2023
An AI platform AI Platform to train your machine learning models at scale, to host your trained model in the cloud, and to use your model to make predictions about new data
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…
MR
Oct 2, 2023
Works very well for small setups, but can be difficult to optimize without the right know-how
While ML Studio does give you the ability to run a lot of transformations, it struggles when the transformations are a bit more complex, when your entire process is transformation-heavy, and when your datasets need to be distributed or parallel processed. While it offers you the capability of running distributed computing, it relies on the user to configure it. It does not do it automatically as Databricks would. It is up to the user to maximize ML Studio's use. Still, suppose you do not preemptively configure it to run everything in distributed compute or parallel jobs. In that case, it will just provision a single compute cluster and take longer than other solutions that do that automatically. ML Studio relies on user configuration to run parallel or distributed jobs. When you are new and trying to experiment with it, it could make your workflows much more costly and longer than they should be.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"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."
"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."
"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."
"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."
"The solution is able to read 90% of the documents correctly with a 10% error rate."
"I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms."
"The initial setup is very straightforward."
"The most valuable feature of Azure Machine Learning Studio for me is its convenience. I can quickly start using it without setting up the environment or buying a lot of devices."
"The product supports open-source tools."
"The solution is very fast and simple for a data science solution."
"The initial setup is very simple and straightforward."
"The solution is really scalable."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"The product is well organized. The thing is how we will get the models to work within our code. We have some suggestions there, but we want to gain more experience and be ready to answer that because we are currently working on this and don't have all the answers yet. The tool is well organized. What I am very happy about is the ease of deploying new resources. You can easily create your pipeline within minutes."
"The drag-and-drop interface is good."
 

Cons

"It could be more clear, and sometimes there are errors that I don't quite understand."
"Customizations are very difficult, and they take time."
"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."
"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."
"The initial setup was straightforward for me but could be difficult for others."
"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."
"The solution can be improved by simplifying the process to make your own models."
"There's room for improvement in terms of binding the integration with Azure DevOps."
"The data preparation capabilities need to be improved."
"Overall, the icons in the solution could be improved to provide better guidance to users. Additionally, the setup process for the solution could be made easier."
"The initial setup time of the containers to run the experiment is a bit long."
"This solution could be improved if they could integrate the data pipeline scheduling part for their interface."
"It is not easy. It is a complex solution. It takes some time to get exposed to all the concepts. We're trying to have a CI/CD pipeline to deploy a machine learning model using negative actions. It was not easy. The components that we're using might have something to do with this."
"The solution cannot connect to private block storage."
"Microsoft Azure Machine Learning Studio could improve in providing more efficient and cost-effective access to its tools for companies like mine."
 

Pricing and Cost Advice

"For every thousand uses, it is about four and a half euros."
"The licenses are cheap."
"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."
"The price of the solution is competitive."
"The pricing is on the expensive side."
"Last year, we paid 60,000 for Microsoft Azure Machine Learning Studio in our department."
"I used the free student license for a few months to operate the solution, but I'll have to pay for it if I want to do more now."
"There isn’t any such expensive costs and only a standard license is required."
"There is a lack of certainty with the solution's pricing."
"When we got our first models and were ready for the user acceptance testing, our licensing fees were between €2,500 ($2,750 USD) and €3,000 ($3,300 USD) monthly."
"I rate the solution's pricing a four on a scale of one to ten, where one is cheap, and ten is expensive."
"There is a license required for this solution."
"The pricing for Microsoft products can be complex due to changes and being cloud-based, so it's not straightforward. I've been familiar with it for years, but sometimes details about product licenses and distribution can be unclear. For Microsoft Azure Machine Learning Studio specifically, I would rate the price a six out of ten."
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Top Industries

By visitors reading reviews
Computer Software Company
13%
Financial Services Firm
11%
Manufacturing Company
10%
University
9%
Financial Services Firm
13%
Computer Software Company
11%
Manufacturing Company
9%
Healthcare Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about Google Cloud AI Platform?
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...
What is your primary use case for Google Cloud AI Platform?
It's a host of use cases depending on, again, the the client requirement.
Which do you prefer - Databricks or Azure Machine Learning Studio?
Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...
What do you like most about Microsoft Azure Machine Learning Studio?
The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem.
 

Also Known As

No data available
Azure Machine Learning, MS Azure Machine Learning Studio
 

Learn More

Video not available
 

Overview

 

Sample Customers

Carousell
Walgreens Boots Alliance, Schneider Electric, BP
Find out what your peers are saying about Google Cloud AI Platform vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: May 2024.
791,948 professionals have used our research since 2012.