Google Cloud Datalab vs Microsoft Azure Machine Learning Studio comparison

Google Cloud Datalab vs. Microsoft Azure Machine Learning Studio

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Review summaries and opinions

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

Categories and Ranking

Google Cloud Datalab

Ranking in Data Science Platforms

18th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Visualization (16th)

Microsoft Azure Machine Lea...

Ranking in Data Science Platforms

8th

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

AI Development Platforms (6th)

Mindshare comparison

As of June 2026, in the Data Science Platforms category, the mindshare of Google Cloud Datalab is 1.7%, up from 1.0% compared to the previous year. The mindshare of Microsoft Azure Machine Learning Studio is 2.8%, down from 5.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Mindshare Distribution
Product	Mindshare (%)
Microsoft Azure Machine Learning Studio	2.8%
Google Cloud Datalab	1.7%
Other	95.5%

Data Science Platforms

Featured Reviews

LijomonJose

System Architect at UST Global EspaÃ±a

dashboards are good and data visualization is more meaningful for the end-user

Access is always via URL, and unless your network is fast, it would be a little tough in India. In India, if we had a faster network, it would be easier. In a big data environment, like when forcing your database with over a billion records, it can be tough for the end-user to manage the data. You need to have a single entity system in each environment. It's not because of GCP, but it would be great to have options like MongoDB or other similar tools in GCP. Then, we wouldn't always need to connect to the cloud and execute SQL queries. Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated. Once the data is collected, it should be easily sorted.

Read full review

reviewer2722962

Data Scientist

Platform accelerates model development, enhances collaboration, and offers efficient deployment

The best features Microsoft Azure Machine Learning Studio offers include deep integration with Python notebooks and Azure Data Lake, which allows me to import external data, and through the pipeline, I can build my models, performing what is called data injection for my model building, making that deep integration quite interesting to use. Microsoft Azure Machine Learning Studio is a powerful platform for those already in the Azure ecosystem because it allows for scalability and provides a good environment for reproducibility, as well as collaboration tools, all designed and packaged in one place, which makes it outstanding. Microsoft Azure Machine Learning Studio has positively impacted my organization by reducing our project delivery times and increasing the pace at which we work, allowing us to focus on other more important tasks. Using Microsoft Azure Machine Learning Studio has reduced our model development time from approximately four hours to about two hours.

Read full review

Quotes from Members

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

Pros

"Google Cloud Datalab is very customizable."

"All of the features of this product are quite good."

"The infrastructure is highly reliable and efficient, contributing to a positive experience."

"The APIs are valuable."

"For me, it has been a stable product."

"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."

"The most valuable feature of the solution is the availability of ChatGPT in the solution."

"It's good for citizen data scientists, but also, other people can use Python or .NET code."

"In terms of what I found most valuable in Microsoft Azure Machine Learning Studio, I especially love the designer because you can just drag and drop items there and apply the logic that's already available with the designer. I love that I can use the libraries in Microsoft Azure Machine Learning Studio, so I don't have to search for the algorithms and all the relevant libraries because I can see them directly on the designer just by dragging and dropping. Though there's a bit of work during data cleansing, that's normal and can't be avoided. At least it's easy to find the relevant algorithm, apply that algorithm to the data, then get the desired output through Microsoft Azure Machine Learning Studio. I also like the API feature of the solution which is readily available for me to expose the output to any consuming application, so that takes out a lot of headache. Otherwise, I have to have a developer who knows the API, and I have to have an API app, so all that is completely taken care of by the Microsoft Azure Machine Learning Studio designer. With the solution, I can concentrate on how to improve the data quality to get quality recommendations, so this lets me concentrate on my job rather than focusing on the regular development of APIs or the pipelines, in particular, the data pipelines pulling the data from other sources. All the data is taken care of and you can also concentrate on other required auxiliary activities rather than just concentrating on machine learning."

"The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure."

"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."

"What I like best about Microsoft Azure Machine Learning Studio is that it's a straightforward tool and it's easy to use. Another valuable feature of the tool is AutoML which lets you get better metrics to train the model right and with good accuracy. The AutoML feature allows you to simply put in your data, and it'll pre-process and create a more accurate model for you. You don't have to do anything because AutoML in Microsoft Azure Machine Learning Studio will take care of it."

"I've developed a couple of chatbots using Azure OpenAI, leveraging its documented solutions and APIs. The tools available make it straightforward to implement machine learning solutions. However, there are challenges, such as hallucinations and security issues, but overall, it works well and is quite fast, allowing for the development of interesting projects."

"It helps in building customized models, which are easy for clients to use."

More Microsoft Azure Machine Learning Studio pros

Cons

"Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated."

"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."

"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."

"The interface should be more user-friendly."

"The product must be made more user-friendly."

"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."

"I would like to see modules to handle Deep Learning frameworks."

"One area where Azure Machine Learning Studio could improve is its user interface structure."

"The solution must increase the amount of data sources that can be integrated."

"In future releases, I would like to see better integration with Power BI within Microsoft Azure Machine Learning Studio."

"Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me."

"For data science professionals or programmers I would rate this solution a four out of 10."

"The solution should be more customizable. There should be more algorithms."

"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."

More Microsoft Azure Machine Learning Studio cons

Pricing and Cost Advice

"It is affordable for us because we have a limited number of users."

"The pricing is quite reasonable, and I would give it a rating of four out of ten."

"The product is cheap."

"We pay only the Azure costs for what we use, which involves some subscription costs. But essentially, you pay for what you use. There are no extra costs in addition to the standard licensing fees."

"There is a license required for this solution."

"Last year, we paid 60,000 for Microsoft Azure Machine Learning Studio in our department."

"The product's pricing is reasonable."

"I am paying for it following a pay-as-you-go. So, the more I use it, the more it costs."

"The product is not that expensive."

"I rate the solution's pricing a four on a scale of one to ten, where one is cheap, and ten is expensive."

"I rate the product price as a nine on a scale of one to ten, where ten means it is very expensive."

More Microsoft Azure Machine Learning Studio pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Construction Company

18%

University

Outsourcing Company

Financial Services Firm

14%

Manufacturing Company

Performing Arts

Construction Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	23
Midsize Enterprise	6
Large Enterprise	32

Questions from the Community

What needs improvement with Google Cloud Datalab?

What is your primary use case for Google Cloud Datalab?

It's for our daily data processing, and there's a batch job that executes it. The process involves more than ten servers or systems. Some of them use a mobile network, some are ONTAP networks, and ...

What advice do you have for others considering Google Cloud Datalab?

Overall, I would rate it a nine out of ten. Google Cloud is very good. Once you go through the features of Google Cloud, it's a good idea to get a GCP certification so you have an idea of how it ca...

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 is your experience regarding pricing and costs for Microsoft Azure Machine Learning Studio?

The pricing for Microsoft Azure Machine Learning Studio is reasonable since it's pay as you go, meaning it won't cost excessively unless specific resources are used.

What needs improvement with Microsoft Azure Machine Learning Studio?

The initial setup can be a bit challenging for someone new, as the learning curve can be steep, but once I master the platform, I find it quite manageable. I would love to see the integration of a ...

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Product Reports

Google Cloud Datalab

Download Google Cloud Datalab product report

Microsoft Azure Machine Learning Studio

Download Microsoft Azure Machine Learning Studio product report

Microsoft Azure Machine Learning Studio report

Also Known As

No data available

Azure Machine Learning, MS Azure Machine Learning Studio

Overview

Google Cloud Datalab offers an integrated environment for seamless data processing and analysis. It combines robust infrastructure with free call-up features to enhance user experience, making it a go-to choice for data-driven tasks.

Google Cloud Datalab is geared towards users seeking efficient data handling solutions. It provides a seamless setup with robust infrastructure, focusing on enhancing APIs and offering meaningful data visualization through its dashboards. Notable AI capabilities include auto-completion and data logging, although some minor configuration challenges exist. While transitioning from AWS can be complex, the platform supports dynamic data pipeline design that suits Python development, offering an end-user friendly environment.

What are the key features of Google Cloud Datalab?

Robust Infrastructure: Provides seamless setup with strong backend support.
Free Call-Up: Allows use without extra costs, enhancing accessibility.
Data Visualization: Dashboards offer meaningful insights into data trends.
AI Capabilities: Features auto-completion and data logging for efficiency.
Customizable Architecture: Adapts to different data handling needs effectively.

What benefits should users expect from Google Cloud Datalab?

Efficient Data Analysis: Streamlines exploratory data analysis in machine learning.
Enhanced API Value: API integrations add significant value to data handling.
Friendly Python Environment: Suitable for end-users looking for ease in Python development.
Adaptive Pipeline Design: Supports complex data transitions from AWS.
Global Availability: Helps overcome time lag issues with server location selection.

In specific industries, Google Cloud Datalab is instrumental in managing data analysis, machine learning exploration, and dataset preprocessing. It facilitates the transfer of workloads from AWS and ensures efficient daily data processing. Organizations benefit from its capability to provision machine learning models into Vertex AI, bolstering research and development efforts. The global availability feature plays a significant role in selecting optimal server locations, addressing time lag and connectivity challenges.

Google

Microsoft Azure Machine Learning Studio offers a drag-and-drop interface, seamless integration with tools, and compatibility with multiple programming languages, making it user-friendly and efficient for developing and deploying machine-learning models.

This platform supports Python, R, and more, with automation features like AutoML, scalable resources, and cognitive services enhance data normalization and deployment. Users can easily create models, integrate with Azure services, and accelerate data science projects using its comprehensive library. Despite its benefits, improvements are suggested for cross-platform integration, enhanced data preparation, and clearer pricing strategies. Users also look for better deployment flexibility, more algorithm options, and examples. Expanded AI features, increased accessibility, and DevOps integration would further benefit its users.

What are the main features of Microsoft Azure Machine Learning Studio?

Drag-and-Drop Interface: Simplifies model creation and experimentation.
Integration with Tools: Works seamlessly with Azure services.
Compatibility with Languages: Supports Python, R, among others.
AutoML: Automates machine learning processes.
Scalable Resources: Provides adaptability in modeling.
Cognitive Services: Enhances data processing capabilities.

What benefits and ROI should users look for?

User-Friendly Design: Encourages accessibility for non-experts.
Seamless Deployment: Eases transition from model development to deployment.
Comprehensive Library: Accelerates data science processes.
Predictive Analytics: Aids in forecasting trends and behaviors.
Fraud Detection: Enhances security measures effectively.

In healthcare, finance, and retail, users apply Microsoft Azure Machine Learning Studio for developing machine learning models, predictive analytics, and deploying models efficiently. Its low-code interface supports experimentation for customer behavior prediction, data analytics, fraud detection, and automated machine learning, facilitating their business and research applications.

Microsoft

Sample Customers

Information Not Available

Walgreens Boots Alliance, Schneider Electric, BP

Google Cloud Datalab vs. Microsoft Azure Machine Learning Studio