Amazon SageMaker vs. Jupyter Notebook

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Amazon SageMaker
Score 8.3 out of 10
N/A
Amazon SageMaker 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.N/A
Jupyter Notebook
Score 8.7 out of 10
N/A
Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine learning. It supports over 40 programming languages, and notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. It is used with JupyterLab, a web-based IDE for…N/A
Pricing
Amazon SageMakerJupyter Notebook
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Amazon SageMakerJupyter Notebook
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Amazon SageMakerJupyter Notebook
Considered Both Products
Amazon SageMaker

No answer on this topic

Jupyter Notebook
Chose Jupyter Notebook
I selected Jupyter Notebook because this is better integrated with the existing production systems than optional tools (for example, R). It is also commonly used tool within the scientist community.
Top Pros
Top Cons
Features
Amazon SageMakerJupyter Notebook
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Amazon SageMaker
-
Ratings
Jupyter Notebook
8.5
21 Ratings
1% above category average
Connect to Multiple Data Sources00 Ratings9.021 Ratings
Extend Existing Data Sources00 Ratings9.220 Ratings
Automatic Data Format Detection00 Ratings8.514 Ratings
MDM Integration00 Ratings7.415 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Amazon SageMaker
-
Ratings
Jupyter Notebook
9.6
21 Ratings
13% above category average
Visualization00 Ratings9.621 Ratings
Interactive Data Analysis00 Ratings9.621 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Amazon SageMaker
-
Ratings
Jupyter Notebook
9.0
21 Ratings
9% above category average
Interactive Data Cleaning and Enrichment00 Ratings9.320 Ratings
Data Transformations00 Ratings8.921 Ratings
Data Encryption00 Ratings8.514 Ratings
Built-in Processors00 Ratings9.314 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Amazon SageMaker
-
Ratings
Jupyter Notebook
8.9
21 Ratings
5% above category average
Multiple Model Development Languages and Tools00 Ratings9.020 Ratings
Automated Machine Learning00 Ratings9.218 Ratings
Single platform for multiple model development00 Ratings9.221 Ratings
Self-Service Model Delivery00 Ratings8.020 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Amazon SageMaker
-
Ratings
Jupyter Notebook
8.8
19 Ratings
3% above category average
Flexible Model Publishing Options00 Ratings8.819 Ratings
Security, Governance, and Cost Controls00 Ratings8.718 Ratings
Best Alternatives
Amazon SageMakerJupyter Notebook
Small Businesses
Google Cloud AI
Google Cloud AI
Score 8.4 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
Medium-sized Companies
Google Cloud AI
Google Cloud AI
Score 8.4 out of 10
Mathematica
Mathematica
Score 8.3 out of 10
Enterprises
Dataiku
Dataiku
Score 7.9 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Amazon SageMakerJupyter Notebook
Likelihood to Recommend
9.0
(6 ratings)
8.4
(22 ratings)
Usability
-
(0 ratings)
10.0
(1 ratings)
Support Rating
-
(0 ratings)
9.0
(1 ratings)
User Testimonials
Amazon SageMakerJupyter Notebook
Likelihood to Recommend
Amazon AWS
Amazon SageMaker is a great tool for developing machine learning models that take more effort than just point-and-click type of analyses. The software works well with the other tools in the Amazon ecosystem, so if you use Amazon Web Services or are thinking about it, SageMaker would be a great addition. SageMaker is great for consumer insights, predictive analytics, and looking for gems of insight in the massive amounts of data we create. SageMaker is less suitable for analysts who do generally "small" data analyses, and "small" data analyses in today's world can be billions of records.
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Open Source
I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.
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Pros
Amazon AWS
  • Provides enough freedom for experienced data scientists and also for those who just need things done without going much deeper into building models.
  • Customization and easy to alter and change.
  • If you already are an Amazon user, you do not need to transition over to another software.
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Open Source
  • Simple and elegant code writing ability. Easier to understand the code that way.
  • The ability to see the output after each step.
  • The ability to use ton of library functions in Python.
  • Easy-user friendly interface.
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Cons
Amazon AWS
  • The UI can be eased up a bit for use by business analysts and non technical users
  • For huge amount of data pull from legacy solutions, the platform lags a bit
  • Considering ML is an emerging topic and would be used by most of the organizations in future, the pipeline integrations can be optimized
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Open Source
  • Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
  • Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.
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Usability
Amazon AWS
No answers on this topic
Open Source
Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.
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Support Rating
Amazon AWS
No answers on this topic
Open Source
I haven't had a need to contact support. However, all required help is out there in public forums.
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Alternatives Considered
Amazon AWS
Amazon SageMaker comes with other supportive services like S3, SQS, and a vast variety of servers on EC2. It's very comfortable to manage the process and also support the end application by one click hosting option. Also, it charges on the base of what you use and how long you use it, so it becomes less costly compared to others.
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Open Source
With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.
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Return on Investment
Amazon AWS
  • We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
  • We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
  • For our use-cases, serving models is less expensive with SageMaker than bespoke servers.
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Open Source
  • Positive impact: flexible implementation on any OS, for many common software languages
  • Positive impact: straightforward duplication for adaptation of workflows for other projects
  • Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration
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ScreenShots