Google Cloud AI provides modern machine learning services, with pre-trained models and a service to generate tailored models.
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Jupyter Notebook
Score 9.4 out of 10
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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…
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Pricing
Google Cloud AI
Jupyter Notebook
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Google Cloud AI
Jupyter Notebook
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Google Cloud AI
Jupyter Notebook
Features
Google Cloud AI
Jupyter 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 Sources
00 Ratings
10.00 Ratings
Extend Existing Data Sources
00 Ratings
10.00 Ratings
Automatic Data Format Detection
00 Ratings
8.50 Ratings
MDM Integration
00 Ratings
7.40 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
Visualization
00 Ratings
6.00 Ratings
Interactive Data Analysis
00 Ratings
8.00 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 Enrichment
00 Ratings
10.00 Ratings
Data Transformations
00 Ratings
10.00 Ratings
Data Encryption
00 Ratings
8.50 Ratings
Built-in Processors
00 Ratings
9.30 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 Tools
00 Ratings
10.00 Ratings
Automated Machine Learning
00 Ratings
9.20 Ratings
Single platform for multiple model development
00 Ratings
10.00 Ratings
Self-Service Model Delivery
00 Ratings
8.00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.