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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    IBM Watson Discovery

    Score9 out of 10
    N/AIBM offers Watson Discovery, a natural language processing (NLP) application with options to measure sentiment, detect entities, semantic roles, and other concepts.N/A

    Jupyter Notebook

    Score8.6 out of 10
    N/AJupyter 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
    IBM Watson DiscoveryJupyter Notebook
    Editions & Modules
    No answers on this topic
    No answers on this topic
    Offerings
    Pricing Offerings
    IBM Watson DiscoveryJupyter Notebook
    Free Trial
    YesNo
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    YesNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Community Pulse
    IBM Watson DiscoveryJupyter Notebook
    Considered Both Products
    IBM
    No answer on this topic
    Open Source
    No answer on this topic
    Key User Insights
    Would buy again
    81%
    Would buy again
    21 Answers
    100%
    Would buy again
    23 Answers
    Delivers good value for the price
    74%
    Delivers good value for the price
    14 Answers
    100%
    Delivers good value for the price
    23 Answers
    Happy with the feature set
    100%
    Happy with the feature set
    26 Answers
    96%
    Happy with the feature set
    22 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    18 Answers
    100%
    Lived up to sales and marketing promises
    17 Answers
    Implementation went as expected
    100%
    Implementation went as expected
    23 Answers
    95%
    Implementation went as expected
    20 Answers
    Features
    IBM Watson DiscoveryJupyter Notebook
    Platform Connectivity
    Comparison of Platform Connectivity features of IBM Watson Discovery and Jupyter Notebook
    Feature
    IBM Watson Discovery
    -
    Ratings
    Jupyter Notebook
    9.0
    22 Ratings
    7% above category average
    Connect to Multiple Data Sources00 Ratings10.022 Ratings
    Extend Existing Data Sources00 Ratings10.021 Ratings
    Automatic Data Format Detection00 Ratings8.514 Ratings
    MDM Integration00 Ratings7.415 Ratings
    Data Exploration
    Comparison of Data Exploration features of IBM Watson Discovery and Jupyter Notebook
    Feature
    IBM Watson Discovery
    -
    Ratings
    Jupyter Notebook
    7.0
    22 Ratings
    19% below category average
    Visualization00 Ratings6.022 Ratings
    Interactive Data Analysis00 Ratings8.022 Ratings
    Data Preparation
    Comparison of Data Preparation features of IBM Watson Discovery and Jupyter Notebook
    Feature
    IBM Watson Discovery
    -
    Ratings
    Jupyter Notebook
    9.5
    22 Ratings
    15% above category average
    Interactive Data Cleaning and Enrichment00 Ratings10.021 Ratings
    Data Transformations00 Ratings10.022 Ratings
    Data Encryption00 Ratings8.514 Ratings
    Built-in Processors00 Ratings9.314 Ratings
    Platform Data Modeling
    Comparison of Platform Data Modeling features of IBM Watson Discovery and Jupyter Notebook
    Feature
    IBM Watson Discovery
    -
    Ratings
    Jupyter Notebook
    9.3
    22 Ratings
    9% above category average
    Multiple Model Development Languages and Tools00 Ratings10.021 Ratings
    Automated Machine Learning00 Ratings9.218 Ratings
    Single platform for multiple model development00 Ratings10.022 Ratings
    Self-Service Model Delivery00 Ratings8.020 Ratings
    Model Deployment
    Comparison of Model Deployment features of IBM Watson Discovery and Jupyter Notebook
    Feature
    IBM Watson Discovery
    -
    Ratings
    Jupyter Notebook
    10.0
    20 Ratings
    16% above category average
    Flexible Model Publishing Options00 Ratings10.020 Ratings
    Security, Governance, and Cost Controls00 Ratings10.019 Ratings
    Best Alternatives
    IBM Watson DiscoveryJupyter Notebook
    Small Businesses
    Elasticsearch
    Score8.5 out of 10
    RapidMiner
    Score8.9 out of 10
    Medium-sized Companies
    Elasticsearch
    Score8.5 out of 10
    Anaconda
    Score8.8 out of 10
    Enterprises
    Amazon CloudSearch
    Score8.5 out of 10
    IBM Watson Studio
    Score10 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    IBM Watson DiscoveryJupyter Notebook
    Likelihood to Recommend
    8.3
    (26 ratings)
    10.0
    (23 ratings)
    Likelihood to Renew
    9.1
    (2 ratings)
    -
    (0 ratings)
    Usability
    4.8
    (3 ratings)
    10.0
    (2 ratings)
    Support Rating
    10.0
    (2 ratings)
    9.0
    (1 ratings)
    User Testimonials
    IBM Watson DiscoveryJupyter Notebook
    Likelihood to Recommend
    IBM
    Overall, IBM Watson Discovery is an amazing technology that we use with our clients to address various business problems, but the biggest challenge has always been about ingesting, analyzing, enriching, and searching huge collections of documents and allowing our end users and SMEs to be able to search for what they need to reduce the time and efforts spent daily on a manual search through various collections of documents. We have successfully managed to reduce manual work by over 80%, and now our SMEs are being used for the skills they have to gather insights rather than do manual work.
    Incentivized
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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.
    Incentivized
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    Pros
    IBM
    • It is an excellently fast platform with documents and the answers to queries.
    • With automation learning beneficial as it saves time.
    • When searching for a document, everything stays located and easy to find.
    • Acceptance of various documents.
    • It has a quite comfortable Technical support, always available when required.
    Incentivized
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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.
    Incentivized
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    Cons
    IBM
    • I believe AI should be more flexible about providing data. However, it's understandable that you need to provide the details you need in a more specific and detailed way.
    • The interface could use more tweaking. Being new to the program, it was kind of hard to navigate.
    • Luckily, there was a customized feature of the dashboard that I could set up, and having something that you know where you are placed always feels familiar and comfortable.
    Incentivized
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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.
    Incentivized
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    Usability
    IBM
    IBM Watson Discovery has the best user capabilities and easily transform business decision-making portfolio. The automation system saves time used in data analysis as opposed to manual research that consumes a lot of time. The visualization across the dashboard enables my team to interpret complex data and use it to make reliable marketing decisions.
    Incentivized
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    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.
    Incentivized
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    Support Rating
    IBM
    Similar to all IBM Watson and Salesforce product solutions, the overall support would be a 10/10. Their provided FAQ's help with frequently experienced issues and if still unable to figure something out, their customer service representatives are always super responsive. With instant chat functions available, it is easy to ask a quick question rather than sitting on hold.
    Incentivized
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    Open Source
    I haven't had a need to contact support. However, all required help is out there in public forums.
    Incentivized
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    Alternatives Considered
    IBM
    Discovery differs from its competitors due to the better ease of implementation and the high level of natural language recognition, it is equal in integration resources such as API and workflow or process pipeline, but it loses in the price for a high volume of documents and/or research. If you own or plan to use other services from the IBM Watson family, there is no doubt that Watson discovery is your best option. Another important point is if you plan to use a cloud or on-premise service (local server or private cloud).
    Incentivized
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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.
    Incentivized
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    Return on Investment
    IBM
    • We find its Enterprise plan expensive for a country of LATAM. For US or Europe based businesses, looks great.
    • A Big Data and massive queries based company would find the service expensive. Maybe a flat price plan would be helpful.
    • Have you thought in making a cheaper plan where you take the learning from your customer's data to enrich your AI tool?
    Incentivized
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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
    Incentivized
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    ScreenShots