Amazon SageMaker vs Microsoft Azure Machine Learning Studio comparison

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Amazon Web Services (AWS) Logo
10,980 views|8,670 comparisons
84% willing to recommend
Microsoft Logo
13,354 views|10,922 comparisons
92% willing to recommend
Comparison Buyer's Guide
Executive Summary

We performed a comparison between Amazon SageMaker 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 Amazon SageMaker vs. Microsoft Azure Machine Learning Studio Report (Updated: May 2024).
772,679 professionals have used our research since 2012.
Q&A Highlights
Question: What are the pros and cons of Amazon SageMaker vs Microsoft Azure Machine Learning Studio?
Answer: Amazon SageMaker and Microsoft Azure Machine Learning Studio are cloud-based platforms for building, training, and deploying Machine Learning models. However, there are some differences between the two platforms, as mentioned below: Amazon SageMaker Pros: Has a wide range of features Scalable Provides community support Cons: Reportedly complex to use May be costly Requires some knowledge of Machine Learning Microsoft Azure Machine Learning Studio Pros: Easy to use Has a Drag-and-drop interface Comes with various pre-built templates and modules May be more affordable than Amazon SageMaker Cons: They say it has less flexibility compared to Amazon SageMaker. May not be as scalable as Amazon SageMaker Community support may not be as extensive The best platform for you will depend on your specific requirements and/or needs. Suppose you are a data scientist with extensive experience in Machine Learning. In that case, it may be best to consider Amazon SageMaker. Still, if you are a beginner or intermediate at Machine Learning, Microsoft Azure Machine Learning Studio may be more suitable for you.
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 solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides.""We were able to use the product to automate processes.""We've had no problems with SageMaker's stability.""The deployment is very good, where you only need to press a few buttons.""The solution is easy to scale...The documentation and online community support have been sufficient for us so far.""The few projects we have done have been promising.""The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework.""The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."

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"Their web interface is good.""I find Microsoft Azure Machine Learning Studio advantageous because it allows integration with Titan Scratch and offers an easy-to-use drag-and-drop menu for developing machine learning models.""Their support is helpful.""The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure.""The initial setup is very simple and straightforward.""It's a great option if you are fairly new and don't want to write too much code.""It's easy to use.""The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps."

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Cons
"The solution requires a lot of data to train the model.""The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV.""The solution is complex to use.""There are other better solutions for large data, such as Databricks.""Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process.""Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier.""I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox.""In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user."

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"Enable creating ensemble models easier, adding more machine learning algorithms.""The data cleaning functionality is something that could be better and needs to be improved.""In terms of data capabilities, if we compare it to Google Cloud's BigQuery, we find a difference. When fetching data from web traffic, Google can do a lot of processing with small queries or functions.""We can create a label job, but we still have to use the Azure Machine Learning REST APIs, which are not yet supported in the Python SDK version 2.""The solution cannot connect to private block storage.""The product must improve its documentation.""I think they should improve two things. They should make their user interface more user-friendly. Integration could also be better. Because Microsoft Machine Learning is a Microsoft product, it's fully integrated with Microsoft Azure but not fully supported for other platforms like IBM or AWS or something else.""This solution could be improved if they could integrate the data pipeline scheduling part for their interface."

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Pricing and Cost Advice
  • "The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."
  • "The support costs are 10% of the Amazon fees and it comes by default."
  • "SageMaker is worth the money for our use case."
  • "Databricks solution is less costly than Amazon SageMaker."
  • "I would rate the solution's price a ten out of ten since it is very high."
  • "There is no license required for the solution since you can use it on demand."
  • "I rate the pricing a five on a scale of one to ten, where one is the lowest price, and ten is the highest price. The solution is priced reasonably. There is no additional cost to be paid in excess of the standard licensing fees."
  • "You don't pay for Sagemaker. You only pay for the compute instances in your storage."
  • More Amazon SageMaker 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:We researched AWS SageMaker, but in the end, we chose Databricks Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It… more »
    Top Answer:We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for… more »
    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 »
    Top Answer:The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem.
    Top Answer:I would rate the costliness of the solution as a nine out of ten.
    Ranking
    5th
    Views
    10,980
    Comparisons
    8,670
    Reviews
    12
    Average Words per Review
    538
    Rating
    7.3
    2nd
    Views
    13,354
    Comparisons
    10,922
    Reviews
    28
    Average Words per Review
    515
    Rating
    7.8
    Comparisons
    Also Known As
    AWS SageMaker, SageMaker
    Azure Machine Learning, MS Azure Machine Learning Studio
    Learn More
    Overview

    Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

    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
    DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
    Walgreens Boots Alliance, Schneider Electric, BP
    Top Industries
    REVIEWERS
    Computer Software Company22%
    Manufacturing Company11%
    Logistics Company11%
    Transportation Company11%
    VISITORS READING REVIEWS
    Financial Services Firm17%
    Educational Organization13%
    Computer Software Company11%
    Manufacturing Company8%
    REVIEWERS
    Financial Services Firm16%
    Energy/Utilities Company12%
    Computer Software Company8%
    Manufacturing Company8%
    VISITORS READING REVIEWS
    Financial Services Firm12%
    Computer Software Company10%
    Manufacturing Company8%
    Healthcare Company7%
    Company Size
    REVIEWERS
    Small Business15%
    Midsize Enterprise40%
    Large Enterprise45%
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise18%
    Large Enterprise68%
    REVIEWERS
    Small Business35%
    Midsize Enterprise10%
    Large Enterprise55%
    VISITORS READING REVIEWS
    Small Business18%
    Midsize Enterprise11%
    Large Enterprise71%
    Buyer's Guide
    Amazon SageMaker vs. Microsoft Azure Machine Learning Studio
    May 2024
    Find out what your peers are saying about Amazon SageMaker vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: May 2024.
    772,679 professionals have used our research since 2012.

    Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 53 reviews. Amazon SageMaker is rated 7.4, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Amazon SageMaker writes "Easy to use and manage, but the documentation does not have a lot of information". 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". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Google Cloud AI Platform, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and IBM SPSS Statistics. See our Amazon SageMaker vs. Microsoft Azure Machine Learning Studio report.

    See our list of best Data Science Platforms vendors and best AI Development Platforms vendors.

    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.