Google Cloud Datalab vs Microsoft Azure Machine Learning Studio comparison

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Executive Summary

We performed a comparison between Google Cloud Datalab and Microsoft Azure Machine Learning Studio based on real PeerSpot user reviews.

Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
To learn more, read our detailed Google Cloud Datalab vs. Microsoft Azure Machine Learning Studio Report (Updated: March 2024).
765,386 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The APIs are valuable.""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.""In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."

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"Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing.""It is a scalable solution…It is a pretty stable solution…The solution's initial setup process was pretty straightforward.""It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component.""The UI is very user-friendly and that AI is easy to use.""Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently.""The AutoML is helpful when you're starting to explore the problem that you're trying to solve.""The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout.""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."

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Cons
"The interface should be more user-friendly.""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.""The product must be made more user-friendly.""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.""Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."

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"There's room for improvement in terms of binding the integration with Azure DevOps.""It would be great if the solution integrated Microsoft Copilot, its AI helper.""It would be nice if the product offered more accessibility in general.""Microsoft should also include more examples and tutorials for using this product.​""A problem that I encountered was that I had to pay for the model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer.""If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice.""Enable creating ensemble models easier, adding more machine learning algorithms.""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."

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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."
  • More Google Cloud Datalab Pricing and Cost Advice →

  • "To use MLS is fairly cheap. Even the paid account is something like $20/month, unless you are provisioning large numbers of VMs for a Hadoop cluster. The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API. If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS."
  • "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."
  • "From a developer's perspective, I find the price of this solution high."
  • "The licensing cost is very cheap. It's less than $50 a month."
  • "There is a license required for this solution."
  • "I am paying for it following a pay-as-you-go. So, the more I use it, the more it costs."
  • "In terms of pricing, for any cloud solution, you should know the tricks of the trade and how to use it, otherwise, you'll end up paying a lot of money irrespective of the cloud provider, so at least for Microsoft Azure Machine Learning Studio pricing versus AWS, I would rate it three out of five, with one being the most expensive, and five being the cheapest. It could be cheaper, but you also have to be careful when choosing the plans, for example, consider the architecture and a lot of other factors before choosing your plan, if you don't want to end up paying more. If your cloud provider has an optimizer that seems to be available in every provider, that would keep alerting you in terms of resources not being used as much, then that would help you with budgeting."
  • "My team didn't deal with the licensing for Microsoft Azure Machine Learning Studio, so I'm unable to comment on pricing, but the money that was spent on the tool was worth it."
  • More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:The product must be made more user-friendly. Sometimes, we have to go a roundabout way and read a lot of instruction that isn't necessary. Generally, if people use the information, they have some… more »
    Top Answer:The solution is really useful. It’s an easy way to get information. I use it as a reference for analytics, sourcing information, and research.
    Top Answer: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… more »
    Ranking
    13th
    Views
    1,837
    Comparisons
    1,686
    Reviews
    2
    Average Words per Review
    631
    Rating
    8.0
    2nd
    Views
    15,820
    Comparisons
    12,975
    Reviews
    23
    Average Words per Review
    494
    Rating
    7.6
    Comparisons
    Also Known As
    Azure Machine Learning, MS Azure Machine Learning Studio
    Learn More
    Overview

    Cloud Datalab is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. It runs on Google Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks.

    Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.

    It has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models.

    Microsoft Azure Machine Learning Will Help You:

    • Rapidly build and train models
    • Operationalize at scale
    • Deliver responsible solutions
    • Innovate on a more secure hybrid platform

    With Microsoft Azure Machine Learning You Can:

    • Prepare data: Microsoft Azure Machine Learning Studio offers data labeling, data preparation, and datasets.
    • Build and train models: Includes notebooks, Visual Studio Code and Github, Automated ML, Compute instance, a drag-and-drop designer, open-source libraries and frameworks, customizable dashboards, and experiments
    • Validate and deploy: Manage endpoints, automate machine learning workflows (pipeline CI/CD), optimize models, access pre-built container images, share and track models and data, train and deploy models across multi-cloud and on-premises.
    • Manage and monitor: Track, log, and analyze data, models, and resources; Detect drift and maintain model accuracy; Trace ML artifacts for compliance; Apply quota management and automatic shutdown; Leverage built-in and custom policies for compliance management; Utilize continuous monitoring with Azure Security Center.

    Microsoft Azure Machine Learning Features:

    • Easy & flexible building interface: Execute your machine learning development through the Microsoft Azure Machine Learning Studio using drag-and-drop components that minimize the code development and straightforward configuration of properties. By being so flexible, the solution also helps build, test ,and generate advanced analytics based on the data.
    • Wide range of supported algorithms: Configuration is simple and easy because Microsoft Azure ML offers readily available well-known algorithms. There is also no limit in importing training data, and the solution enables you to fine-tune your data easily, saving money and time and helping you generate more revenue.
    • Easy implementation of web services: Simply drag and drop your data sets and algorithms, and link them together to implement web services. It only requires one click to create and publish the web service, which can be used from any device by passing valid credentials.
    • Great documentation: Microsoft Azure provides full stacks of documentation, such as tutorials, quick starts, references, and many other resources that help you understand how to easily build, manage, deploy, and access machine learning solutions effectively.

    Microsoft Azure Machine Learning Benefits:

    • It is fully integrated with Python and R SDKs.
    • It has an updated drag-and-drop interface, generally known as Azure Machine Learning Designer.
    • It supports MLPipelines, where you can build flexible and modular pipelines to automate workflows.
    • It supports multiple model formats depending upon the job type.
    • It has automated model training and hyperparameter tuning with code-first and no-code options.
    • It supports data labeling projects.

    Reviews from Real Users:

    "The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout.” - Channing S.l, Owner at Channing Stowell Associates

    "The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses.” - Chris P., Tech Lead at a tech services company

    "The UI is very user-friendly and the AI is easy to use.” - Mikayil B., CRM Consultant at a computer software company

    "The solution is very fast and simple for a data science solution.” - Omar A., Big Data & Cloud Manager at a tech services company

    Sample Customers
    Information Not Available
    Walgreens Boots Alliance, Schneider Electric, BP
    Top Industries
    VISITORS READING REVIEWS
    Financial Services Firm16%
    Educational Organization12%
    Computer Software Company11%
    Manufacturing Company9%
    REVIEWERS
    Financial Services Firm17%
    Energy/Utilities Company13%
    Comms Service Provider9%
    Retailer9%
    VISITORS READING REVIEWS
    Financial Services Firm12%
    Computer Software Company10%
    Manufacturing Company8%
    Healthcare Company7%
    Company Size
    VISITORS READING REVIEWS
    Small Business23%
    Midsize Enterprise10%
    Large Enterprise68%
    REVIEWERS
    Small Business30%
    Midsize Enterprise11%
    Large Enterprise59%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise11%
    Large Enterprise71%
    Buyer's Guide
    Google Cloud Datalab vs. Microsoft Azure Machine Learning Studio
    March 2024
    Find out what your peers are saying about Google Cloud Datalab vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: March 2024.
    765,386 professionals have used our research since 2012.

    Google Cloud Datalab is ranked 13th in Data Science Platforms with 5 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 47 reviews. Google Cloud Datalab is rated 7.6, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Google Cloud Datalab writes "Easy to setup, stable and easy to design data pipelines". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". Google Cloud Datalab is most compared with Databricks, IBM SPSS Statistics, Cloudera Data Science Workbench, Domino Data Science Platform and Qlik Sense, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Google Vertex AI, Azure OpenAI, TensorFlow and Google Cloud AI Platform. See our Google Cloud Datalab vs. Microsoft Azure Machine Learning Studio report.

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    We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.