Google Cloud AI vs. Jupyter Notebook

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google Cloud AI
Score 8.7 out of 10
N/A
Google Cloud AI provides modern machine learning services, with pre-trained models and a service to generate tailored models.N/A
Jupyter Notebook
Score 9.4 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
Google Cloud AIJupyter Notebook
Editions & Modules
No answers on this topic
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Offerings
Pricing Offerings
Google Cloud AIJupyter 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
Google Cloud AIJupyter Notebook
Features
Google Cloud AIJupyter Notebook
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Google Cloud AI
-
Ratings
Jupyter Notebook
9.0
Ratings
7% above category average
Connect to Multiple Data Sources00 Ratings10.00 Ratings
Extend Existing Data Sources00 Ratings10.00 Ratings
Automatic Data Format Detection00 Ratings8.50 Ratings
MDM Integration00 Ratings7.40 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Google Cloud AI
-
Ratings
Jupyter Notebook
7.0
Ratings
18% below category average
Visualization00 Ratings6.00 Ratings
Interactive Data Analysis00 Ratings8.00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Google Cloud AI
-
Ratings
Jupyter Notebook
9.5
Ratings
15% above category average
Interactive Data Cleaning and Enrichment00 Ratings10.00 Ratings
Data Transformations00 Ratings10.00 Ratings
Data Encryption00 Ratings8.50 Ratings
Built-in Processors00 Ratings9.30 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Google Cloud AI
-
Ratings
Jupyter Notebook
9.3
Ratings
10% above category average
Multiple Model Development Languages and Tools00 Ratings10.00 Ratings
Automated Machine Learning00 Ratings9.20 Ratings
Single platform for multiple model development00 Ratings10.00 Ratings
Self-Service Model Delivery00 Ratings8.00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Google Cloud AI
-
Ratings
Jupyter Notebook
10.0
Ratings
16% above category average
Flexible Model Publishing Options00 Ratings10.00 Ratings
Security, Governance, and Cost Controls00 Ratings10.00 Ratings
Best Alternatives
Google Cloud AIJupyter Notebook
Small Businesses
InterSystems IRIS
InterSystems IRIS
Score 7.7 out of 10
IBM Watson Studio
IBM Watson Studio
Score 10.0 out of 10
Medium-sized Companies
Posit
Posit
Score 10.0 out of 10
Posit
Posit
Score 10.0 out of 10
Enterprises
Posit
Posit
Score 10.0 out of 10
Posit
Posit
Score 10.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Google Cloud AIJupyter Notebook
Likelihood to Recommend
8.0
(0 ratings)
10.0
(0 ratings)
Likelihood to Renew
10.0
(0 ratings)
-
(0 ratings)
Usability
8.0
(0 ratings)
10.0
(0 ratings)
Support Rating
7.3
(0 ratings)
9.0
(0 ratings)
Implementation Rating
10.0
(0 ratings)
-
(0 ratings)
User Testimonials
Google Cloud AIJupyter Notebook
Likelihood to Recommend
Google Images analysis model is a good one and I think is very useful in our case of detections. Speech AI is also a good one. I can only recommend Google Cloud AI API and the model for that second will be SpeechKit by Yandex both these tools have exceptional values one can utilise to enhance their projects.
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I would rate it 9/10 while recommending Jupyter Notebook as it offers me a wide range of functionality to operate. It is very well suited for someone who is new to python programming as the user interface helps you build code line by line. I personally have written multiple programs in Python using Jupyter Notebook as it helps me organize long code by breaking it in a structure. Also the ability to write comments using '#' helps a lot to a reader understand the code.
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Pros
  • Smart reply and its AI suggestions make the organization think more carefully about their e-mail responses in Gmail. We were skeptical at first but it really works well for many instances.
  • We do a lot of business and contracts in Western Europe and South America, so the translate solutions make this much easier for our banking paperwork.
  • When we go to meetings or during a meeting, we often use the Google voice search to save time on research and filtering ideas or analysis.
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  • Coding and error correction line by line
  • Simple and Effectiveness
  • Easy to use for visualisation and presentation of code
  • Could be used at any place any time without hassle
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Cons
  • Hard to find what to use - To find the right products, you need look closely at the details of each API, and find which suits your purposes. This can be easily fixed by creating a main page that details all of the products simply.
  • Expensive - The API costs can quickly add up, especially during the setup process and as engineers figure out the usage of the API.
  • No playground or training - There is a lack of an "API playground" or training sessions that could make onboarding engineers to this API much easier.
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  • 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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Likelihood to Renew
We are extremely satisfied with the impact that this tool has made on our organization since we have practically moved from crawling to walking in the process of generating information for our main task to investigate in the field through interviews. With the audio to text translation tool there is a difference from heaven to earth in the time of feeding our internal data.
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No answers on this topic
Usability
I give 8 because although it´s a tool I really enjoy working with, I think Google Cloud AI's impact is just starting, therefore I can visualize a lot/space of improvements in this tool. As an example the application of AI in international environments with different languages is a good example of that space/room to improve.
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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
Every rep has been nice and helpful whenever I call for help. One of the systems froze and wouldn't start back up and with the help of our assigned rep we got everything back up in a timely manner. This helped us not lose customers and money.
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I haven't had a need to contact support. However, all required help is out there in public forums.
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Implementation Rating
In fact, you only need the basic tech knowledge to do a Google search. You need to know if your organization requires it or not,. our organization required it. And that is why we acquired it and solved a need that we had been suffering from. This is part of the modernization of an organization and part of its growth as a company.
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Alternatives Considered
Google's documentation for their AI and Machine Learning products is a bit more straightforward and still much easier to onboard into compared to the Azure Machine Learning and other AI products. Additionally, Google's Cloud AI products provide more comprehensive specific use-cases that are API-optimized, and easier to integrate into existing scripts and backends.
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Jupyter Notebook is unique in that it offers a flexible, lightweight, easy-to-replicate way of organizing your code in a visually intuitive fashion that can be exported in a number of formats. I've found that the broad functionalities available within the notebooks suit a lot of needs I have for EDA, modeling, and data export that makes other software products fairly redundant.
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Return on Investment
  • Positive impact on ROI due to reduction in staff needed to build, deploy and manage a AI workload pipeline.
  • Positive impact on the business by moving to OpEx without need for upfront CapEx investment.
  • Improvement in time to analyze the data (structured and unstructured), increasing the business's ability to act based on AI results.
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  • 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